characterization of the galactic white dwarf population in

Characterization of the Galactic White Dwarf

Population in the Next Generation Virgo

Cluster Survey

by

Nicholas Fantin

A thesis submitted to the

Department of Physics, Engineering Physics & Astronomy

in conformity with the requirements for

the degree of Master of Science

Queen’s University

Kingston, Ontario, Canada

August 2016

Copyright c� Nicholas Fantin, 2016

Abstract

Halo white dwarfs remain one of the least studied stellar populations in the Milky

Way because of their faint luminosities. Recent work has uncovered a population

of hot white dwarfs which are thought to be remnants of low-mass Population II

stars. This thesis uses optical data from the Next Generation Virgo Cluster Survey

(NGVS) and ultravoilet data from the GALEX Ultraviolet Virgo Cluster Survey (GU-

ViCS) to select candidates which may belong to this population of recently formed

halo white dwarfs. A colour selection was used to separate white dwarfs from QSOs

and main-sequence stars. Photometric distances are calculated using model colour-

absolute magnitude relations. Proper motions are calculated by using the di↵erence

in positions between objects from the Sloan Digital Sky Survey and the NGVS. The

proper motions are combined with the calculated photometric distances to calculate

tangential velocities, as well as approximate Galactic space velocities. White dwarf

candidates are characterized as belonging to either the disk or the halo using a va-

riety of methods, including calculated scale heights (z> 1 kpc), tangential velocities

(vt

>200 km/s), and their location in (V,U) space. The 20 halo white dwarf can-

didates which were selected using Galactic space velocities are analyzed, and their

colours and temperatures suggest that these objects represent some of the youngest

white dwarfs in the Galactic halo.

i

Acknowledgments

First, I would like to thank my supervisor, David Hanes, and my co-supervisor, Pat

Cote, for their knowledge, direction, and support throughout my time at Queen’s.

Their infectious enthusiasm truly reinforced my love of astronomy.

Thank you to Stephen Gwyn, Luciana Bianchi, and the NGVS team for their contri-

butions to this thesis. I would also like to thank the Queen’s support sta↵ (Loanne,

Tammie, Kyra, Gord, Peg), my fellow graduate students, Terry Bridges, and anyone

else at Queen’s who has helped me out along the way.

I would also like to thank the Dunlap Institute, Suresh Sivanandam, and Stephane

Courteau for organizing the incredible Dunlap Mauna Kea School.

The biggest thank yous go to my Mom, Heather, my Dad, Patrick, my sister, Kathryn,

and my late dog Oreo, for their love and support. I would also like to thank my best

friends Ryan, Benen, and Spencer for all of the great times I had at Queen’s. I am

also grateful for the continued support from my friends in Toronto: Rachelle, Mark,

Djurdja, Madi, and Lyndsay.

Finally, I would like to thank Ashley for thinking I can actually dance. You’ve been

by my side ever since and I couldn’t have done this without you.

ii

Contents

Abstract i

Acknowledgments ii

Contents iii

List of Tables vi

List of Figures vii

Table of Acronyms 1

Chapter 1: Introduction 21.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.2 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.3 Organization of Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Chapter 2: Galactic White Dwarfs 52.1 White Dwarfs and the Study of the Milky Way . . . . . . . . . . . . 52.2 White Dwarf Properties . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.2.1 Formation and Composition . . . . . . . . . . . . . . . . . . . 82.2.2 Spectral Types . . . . . . . . . . . . . . . . . . . . . . . . . . 102.2.3 Physical Properties . . . . . . . . . . . . . . . . . . . . . . . . 12

2.3 White Dwarf Surveys within the Virgo Cluster Region . . . . . . . . 172.3.1 The Sloan Digital Sky Survey . . . . . . . . . . . . . . . . . . 182.3.2 GALEX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

2.4 Selecting Halo White Dwarfs . . . . . . . . . . . . . . . . . . . . . . . 202.4.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202.4.2 Previous Results Using Kinematics . . . . . . . . . . . . . . . 212.4.3 Globular Clusters and the Inner Halo . . . . . . . . . . . . . . 24

Chapter 3: The Data: Constructing an Optical-Ultraviolet Catalog 27

iii

3.1 The Next Generation Virgo Cluster Survey . . . . . . . . . . . . . . . 273.1.1 Extracting Point Sources . . . . . . . . . . . . . . . . . . . . . 31

3.2 The GALEX Ultraviolet Virgo Cluster Survey . . . . . . . . . . . . . 343.2.1 Caveats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

3.3 Matching the Catalogs . . . . . . . . . . . . . . . . . . . . . . . . . . 363.3.1 Creating a UV-Optical Catalog . . . . . . . . . . . . . . . . . 373.3.2 Multiple Matches . . . . . . . . . . . . . . . . . . . . . . . . . 373.3.3 Spurious Matches . . . . . . . . . . . . . . . . . . . . . . . . . 38

Chapter 4: White Dwarf Candidates within the NGVS Field 404.1 Selection Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

4.1.1 Choosing Colours . . . . . . . . . . . . . . . . . . . . . . . . . 404.2 Contaminants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 464.3 Comparison to SDSS-GALEX AIS Matching . . . . . . . . . . . . . . 47

Chapter 5: Photometric Properties 495.1 Magnitude Distributions . . . . . . . . . . . . . . . . . . . . . . . . . 49

5.1.1 Model Comparison . . . . . . . . . . . . . . . . . . . . . . . . 515.2 Photometric Distances . . . . . . . . . . . . . . . . . . . . . . . . . . 545.3 Deriving Proper Motions . . . . . . . . . . . . . . . . . . . . . . . . . 57

5.3.1 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 575.3.2 Estimating the Uncertainty . . . . . . . . . . . . . . . . . . . 585.3.3 Comparison to the USNO Catalog . . . . . . . . . . . . . . . . 585.3.4 Using QSOs . . . . . . . . . . . . . . . . . . . . . . . . . . . . 625.3.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

Chapter 6: Selecting Candidate Halo White Dwarfs 666.1 Colours and Magnitudes: Comparing to the TRILEGAL Model . . . 66

6.1.1 Using g-band Magnitudes . . . . . . . . . . . . . . . . . . . . 676.1.2 Using a Colour-Magnitude Selection . . . . . . . . . . . . . . . 67

6.2 Using Photometric Distances . . . . . . . . . . . . . . . . . . . . . . . 706.3 Kinematics: Using Proper Motions . . . . . . . . . . . . . . . . . . . 70

6.3.1 Tangential Velocities . . . . . . . . . . . . . . . . . . . . . . . 726.3.2 Galactic Space Velocities . . . . . . . . . . . . . . . . . . . . . 74

Chapter 7: Discussion 827.1 Halo White Dwarfs and Stellar Evolution Models . . . . . . . . . . . 827.2 Matching E↵ects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

7.2.1 Photometric . . . . . . . . . . . . . . . . . . . . . . . . . . . . 867.2.2 Astrometric . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

7.3 Sources of Bias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

iv

7.3.3 Radial Velocities . . . . . . . . . . . . . . . . . . . . . . . . . 937.4 Analysis of Halo Candidates . . . . . . . . . . . . . . . . . . . . . . . 93

Chapter 8: Summary and Conclusions 978.1 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100

Bibliography 103

v

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

List of Tables

2.1 Common white dwarf spectral types and their dominant characteris-

tics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

3.1 Various parameters for the NGVS including the exposure times, mag-

nitude limits, seeing, and background constraints. . . . . . . . . . . . 29

6.1 Velocity dispersions for typical thin and thick disk populations in km/s

compared to the dispersions from Figure 6.5 Table: Binney & Merri-

field (1998) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

7.1 Properties of halo white dwarf candidates selected based on their Galac-

tic space velocities. All velocities are in km/s . . . . . . . . . . . . . 94

vi

List of Figures

2.1 Sirius B, a white dwarf, (lower left) and its companion Sirius, an A-type

main sequence star (center). Image: NASA . . . . . . . . . . . . . . 5

2.2 A typical spectrum for white dwarfs with a Hydrogen atmosphere (DA,

top), Helium atmosphere (DB, bottom), and an atmosphere dominated

by metals (DZ, middle). Prominent spectral features are also high-

lighted, such as the Balmer Series, He I, and Ca H&K. Figure: Verbeek

et al (2012) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2.3 Theoretical line profiles for the Balmer series from H�

(bottom) to H8

(top) at various temperatures and log g. The dashed line indicates log

g=7.0 and the solid line is log g=9.0. Figure: Tremblay & Bergeron

(2009). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2.4 Temperature (top) and log g (bottom) distributions for DA and DB

white dwarfs in the SDSS. The surface gravity is in units of cm/s2

Figure: Kleinmann et al. (2013) . . . . . . . . . . . . . . . . . . . . 14

2.5 Mass distribution for disk white dwarfs from Palomar Green Survey

(Top) and SDSS DR7 (Bottom) Figures: Liebert et al. (2005); Klein-

mann et al. (2013) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2.6 The Galactic Space Velocity coordinate system. Figure: Carroll &

Ostlie (2006). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

vii

2.7 U and V velocities in km/s for white dwarfs from (Top) Kawka &

Vennes (2006) and (Bottom) Oppenheimer et al. (2001). The velocity

ellipsoids for a thin disk, a thick disk, and a halo population are plotted

where the disk components are centered at (U,V) = (0,0) and the halo

is at (-220, 0). The squares represent selected halo candidates. Figure:

(Top) Kawka & Vennes (2006), (Bottom) Oppenheimer et al. (2001). 22

2.8 Colour-Magnitude diagram for the globular cluster M4 using HST fil-

ters. The circled region represents recently formed white dwarfs. Fig-

ure: Kalirai (2012) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

3.1 The Next Generation Virgo Cluster Survey (NGVS) field in J2000

equatorial coordinates. The yellow lines correspond to a single Mega-

cam pointing, and the grid totals 117 separate pointings. The back-

ground is composed of real NGVS images and was taken from the

NGVS graphical search tool. . . . . . . . . . . . . . . . . . . . . . . . 27

3.2 The transmission curves for the filters used in the NGVS with a typical

DA white dwarf spectrum for reference (black). . . . . . . . . . . . . 28

3.3 The concentration index for NGVS Sources (grey) and stellar objects

from SDSS (black). A sharp stellar sequence centered on �i = 0, and

a cluster of background galaxies can be seen towards fainter g-band

magnitudes. Only 100,000 point sources from the NGVS are plotted

for clarity. The dashed lines indicate the chosen point source range

used in this thesis and is in agreement with Durrell et al. (2014). . . 31

viii

3.4 The sky coverage for the GUViCS catalog is shown in RA and DEC

(J2000) compared to the NGVS footprint (yellow). The dashed circles

represent the targeted GUViCS pointings, and the colour indicates

the exposure time of the image. The red circles indicate exposure

times between 800 and 1500 seconds, the cyan circles represent images

with MIS depth (exposure time ⇠1500-30 000 seconds), the dark blue

circles indicate images with exposure times greater than 30 000s. Black

areas represent fields that were avoided due to the presence of a bright

foreground star. As a reference, the fields from the far-infrared survey

HeViCS are shown in green. Figure: Voyer et al. (2014). . . . . . . . 34

4.1 Colour-colour diagrams of spectroscopically confirmed objects from the

SDSS that have been matched to the NGVS. The blue points indicate

white dwarfs from Kleinmann et al. (2015), the red points are QSOs

from Paris et al. (2014), the cyan points are blue stragglers and blue

horizontal branch stars from Scibelli et al. (2014), and the stars were

queried using the SQL search in SDSS DR12. The separation between

the objects is poor when compared to the (g-i), (NUV-g) diagram in

Figure 4.2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

4.2 (g - i), (NUV - g) colour-colour diagram for the matched NGVS-

GUViCS point source objects (grey), spectroscopically confirmed SDSS

WDs (blue circles), QSOs (red squares), and stars (magenta triangles).

The black dashed lines indicate the chosen colour cuts applied to the

NGVS-GUViCS data in order to select white dwarf candidates while

minimizing contamination from other objects. . . . . . . . . . . . . . 42

ix

4.3 Model tracks from Bianchi et al. (2009a) are plotted to show ideal

locations for the WDs (blue), QSOs (red), and for stars of various

surface gravities (magenta). The WD track is for a log g = 8.0 for

temperatures between 15,000 and 200,000 K. The model QSO colours

are a function of redshift, with values between 0 and 5. Model stars

with log g = 5, 4, 3 are indicated by the solid, dashed, and dotted

magenta lines, respectively. The black dashed lines indicate the chosen

colour cuts applied to select white dwarf candidates. . . . . . . . . . . 44

5.1 Magnitude distributions for the white dwarf candidates selected in

Chapter 4 compared to the SDSS spectroscopic selection from Klein-

man et al. 2013 (K13) and GALEX AIS photometric selection from

Bianchi et al. (2011) (B11). . . . . . . . . . . . . . . . . . . . . . . . 49

5.2 g-band magnitude distribution for the white dwarf candidates (green)

and the TRILEGAL white dwarfs (white). . . . . . . . . . . . . . . 51

5.3 Photometric distances for the white dwarf candidates selection in Chap-

ter 4.1. Absolute magnitudes were calculated using the TLUSTY

colour-magnitude relationship in combination with the Megacam (g

- i) colours obtained as part of the NGVS. The distances were cal-

culated using the distance modulus with the absolute and apparent

g-band magnitudes as they have the least uncertainty. Error bars are

a result of the uncertainties in the (g-i) colour. . . . . . . . . . . . . . 55

x

5.4 Comparison between proper motions in Right Ascension (RA) derived

using NGVS-SDSS positions and the proper motion catalog given by

Munn et al. (2014). The root-mean-square deviation between the

NGVS and SDSS proper motions in RA is 12.9 mas/yr. . . . . . . . 58

5.5 Comparison between proper motions in Declination (DEC) derived us-

ing NGVS-SDSS positions and the proper motion catalog given by

Munn et al. (2014). The root-mean-square deviation between the

NGVS and SDSS proper motions in DEC is 12.7 mas/yr. . . . . . . . 59

5.6 Comparing the total proper motion measured from the NGVS-SDSS

matching technique to the proper motions derived by Munn et al.

(2014). The root-mean-square deviation about the one-to-one line

(red) is 9.6 mas/yr. . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

5.7 Calculated proper motions for stars (yellow), white dwarfs (blue), and

QSOs (red) using the NGVS-SDSS positions. . . . . . . . . . . . . . 63

5.8 Proper motion in RA and DEC for white dwarf candidates in the NGVS

dataset (black). QSOs are also plotted to show they display minimal

change in position between the two catalogs. . . . . . . . . . . . . . . 64

6.1 The relative contribution to the white dwarf population by each of the

three Galactic components. . . . . . . . . . . . . . . . . . . . . . . . . 67

6.2 A TRILEGAL CMD for white dwarfs from each Galactic component.

The black line was visually chosen to separate halo and disk white dwarfs. 68

6.3 Calculated scale heights as a function of g-band magnitude. The

dashed line indicated the chosen scale height which separates disk and

halo white dwarfs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

xi

6.4 Calculated tangential velocities for the NGVS white dwarf candidates.

The blue line indicates a tangential velocity of 200 km/s, which was

the cut used to separate disk and halo white dwarfs. . . . . . . . . . . 72

6.5 Galactic space velocities for white dwarf candidates, along with the 2�

velocity ellipsoids for the thin disk (dashed), thick disk (dot-dashed),

and the halo (dotted red). The 1� velocity ellipsoid for the halo is

indicated by the black dotted line. . . . . . . . . . . . . . . . . . . . 79

6.6 Galactic space velocities and associated error-bars for white dwarf can-

didates. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

7.1 Disk and Halo contributions calculated from each method described in

Chapter 6. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

7.2 A comparison between IFMRs studied by Bianchi et al. (2011). Figure:

Bianchi et al. (2011). . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

7.3 The di↵erence between the brightest and dimmest objects within a

double match scenario as a function of the magnitude of the brightest

object. The red circle highlights a case where 2 objects of similar

magnitude were matched to a single UV source, whereas the blue circle

highlights objects for which the magnitude of faintest source lies near

the NGVS survey limit. . . . . . . . . . . . . . . . . . . . . . . . . . 86

7.4 The e↵ect the colour selection from Chapter 4 has on the temperature

of the selected white dwarfs. . . . . . . . . . . . . . . . . . . . . . . . 89

7.5 A comparison between the colour-absolute magnitude relationships of a

DA and DB white dwarf in SDSS band-passes from Holberg & Bergeron

(2006). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

xii

7.6 Colour-absolute magnitude relationships for DA white dwarfs with dif-

ferent values of log g. . . . . . . . . . . . . . . . . . . . . . . . . . . 92

7.7 The SDSS spectrum of halo white dwarf candidate 7 with common

spectral lines indicated. The strong, broad, Balmer series is present

indicating that it is a DA. Figure: SDSS DR12 (Alam et al. 2015) . 95

xiii

Table of Acronyms

Table 1: Summary of frequently used acronyms and variables

Acronym MeaningNGVS Next Generation Virgo Cluster SurveyGUViCS GALEX Next Generation Virgo Cluster SurveySDSS Sloan Digital Sky SurveyUSNO United States Naval Observatoryµ Proper motionlog g Surface Gravity in cgs units (cm/s2)WD White dwarfM� Solar MassFWHM Full-Width at Half MaximumCMD Colour-Magnitude DiagramRA Right AscensionDEC DeclinationQSO Quasi-Stellar Object (Quasar)UV Ultraviolet�eff

Central e↵ective wavelengthBHB Blue Horizontal-BranchBS Blue StragglerIFMR Initial-Final Mass RelationshipPSF Point-Spread FunctionAGB Asymptotic Giant BranchRMS Root-Mean-Square

iii

xiv

1

Chapter 1

Introduction

1.1 Motivation

Although 95% of stars end their lives as white dwarfs, the white dwarfs in the Galactic

halo remain largely unstudied (Cojocaru et al. 2015). This is a result of their faint

optical luminosities. Much of our knowledge from this population has come from

the Sloan Digital Sky Survey (SDSS) (York et al. 2000), however, many of the white

dwarfs are expected to be fainter than the survey limits. Moreover, halo white dwarfs,

unlike their main-sequence counterparts, cannot be selected based on their metallicity.

This is because the metals sink below the photosphere on short timescales. The only

reliable method to select halo white dwarfs is through their kinematics.

Recently, Kalirai (2012) discovered a population of hot white dwarfs with halo-like

velocities. These stars are thought to be young white dwarfs that are formed from

low-mass population II progenitors. These objects have temperatures between 15 000

K and 20 000 K. The high temperature of these objects means that their spectral

energy distribution will peak in the ultraviolet. The Next Generation Virgo Cluster

Survey (NGVS) and its ultraviolet compliment, the GALEX Ultraviolet Virgo Cluster

1.2. CONTRIBUTIONS 2

Survey (GUViCS), provide an ideal dataset for selecting and studying these young

halo white dwarfs. This is a result of the photometric depth of both datasets, and

that the large baseline allows for the separation of the white dwarfs from QSOs in

colour-colour space. The NGVS can also be combined with the SDSS to provide

second-epoch optical data, which can be used to derive proper motions. Combining

all of these opportunities will allow for the selection of halo white dwarfs within the

NGVS footprint.

1.2 Contributions

This thesis uses data that was obtained as part of the Next Generation Virgo Cluster

Survey (NGVS), the GALEX Ultraviolet Virgo Cluster Survey (GUViCS), and the

Sloan Digital Sky Survey (SDSS).

The NGVS research group includes faculty, research scientists, post-docs, and

students from Canada, France, and the USA. Team members who contributed to the

data used include Patrick Cote (NRC Herzberg Institute of Astrophysics, Canada),

Laura Ferrarese (NRC Herzberg Institute of Astrophysics, Canada), Stephen Gwyn

(NRC Herzberg Institute of Astrophysics, Canada), Jean-Charles Cuillandre (CFHT,

USA), and David Hanes (Queen’s University, Canada).

The GUViCS team consists of 8 astronomers and is led by Alessandro Boselli (P.I.,

LAM, France) and co-PI: Samuel Boissier (LAM, France).

Model tracks for the white dwarfs, QSOs, and main sequence stars as well as feed-

back and suggestions were provided by Luciana Bianchi (John’s Hopkins University,

Baltimore).

1.3. ORGANIZATION OF THESIS 3

Finally, the proper motion data from SDSS used in this thesis was acquired at the

United States Naval Observatory and published by Munn et al. (2014).

1.3 Organization of Thesis

This thesis is organized as follows: Chapter 2 provides the necessary background

information on white dwarfs and the methods used to detect and characterize them.

Chapter 3 describes the datasets used and the methods employed to construct an

optical-UV catalog within the NGVS footprint. Chapter 4 describes the methods

used to select white dwarf candidates within the NGVS footprint, while Chapter 5

describes various photometric and kinematic properties of the candidates. Chapter 6

uses various methods to characterize each candidate as either a disk or a halo white

dwarf, and Chapter 7 discusses the results. Chapter 8 summarizes the results and

discusses the future prospects for work on halo white dwarfs.

4

Chapter 2

Galactic White Dwarfs

White dwarfs represent the final evolutionary stage of stars with initial masses less

than ⇠8 M� (Doherty et al. 2015) . This means that roughly 95% of stars will

end their lives as a white dwarf, including our Sun (Althaus 2010). This section

motivates the study of white dwarfs, with particular emphasis on those belonging to

the Galactic stellar halo. The observational properties of a white dwarf are presented

in Chapter 2.1, while Chapter 2.2 details the importance of white dwarfs in the study

of the Milky Way. This Chapter concludes by presenting methods and result from

previous surveys which attempted to select white dwarfs in the Galactic stellar halo.

2.1 White Dwarfs and the Study of the Milky Way

The study of white dwarfs began with the discovery of Sirius B (seen in Figure 2.1 ) by

Alvan Graham Clark in 1862, which had previously been hypothesized by Friedrich

Bessel in 1838 (Carroll & Ostlie, 2006). In 1915 the temperature of Sirius B was

measured to be almost three times higher than its companion star, yet the radius was

smaller than that of the Earth. Since the discovery of Sirius B, over 20 000 Galactic

2.1. WHITE DWARFS AND THE STUDY OF THE MILKY WAY 5

Figure 2.1: Sirius B, a white dwarf, (lower left) and its companion Sirius, an A-typemain sequence star (center). Image: NASA

white dwarfs have been discovered (see Chapter 2.3), with the total number left to

be discovered thought to be close to one hundred million (van Oirschot et al. 2014).

White dwarfs are important to the study of the Milky Way because they are

ubiquitous, long-lasting, and have well-studied properties (Cojocaru et al. 2015).

They are also test-beds for physics in extreme environments. Since white dwarfs do

not produce energy via nuclear fusion, a di↵erent type of pressure must exist in order

to prevent gravitational collapse. This pressure is called electron degeneracy pressure

and is a result of the Pauli Exclusion Principle, which states that no two electrons

in a gas can occupy the same quantum state (Winget & Kepler 2008). The electrons

resist collapse as increasing the electron density requires energy to raise an electron

to a higher energy level. Hence, white dwarfs present a real-life application of the

Pauli Exclusion Principle (Van Horn 1979).

2.1. WHITE DWARFS AND THE STUDY OF THE MILKY WAY 6

As the mass of a white dwarf increases, so too does its internal kinetic energy.

This is a result of the Pauli Exclusion Principle. At higher masses, the electrons

become relativistic until they reach the speed of light, which results in a maximum

attainable kinetic energy. This also imposes a maximum mass for which electron

degeneracy pressure can counteract the gravitational collapse. This mass is called

the Chandrasekhar mass and is equal to ⇠1.4M� (Chandrasekhar 1933). In binary

systems mass can be deposited onto the white dwarf by its companion, allowing the

white dwarf to grow in mass. When white dwarfs exceed this mass they tend to

explode as Type 1a supernovae. Since this mass limit is universal, and a result of

the underlying physics within the white dwarf, the resulting supernovae have simi-

lar light-curves. Type Ia supernovae have been used as standard candles to study

the cosmological constant, ⇤, and have shown that the expansion of the universe is

accelerating (Perlmutter et al, 1997, 1999; Schmidt et al. 1998).

White dwarf properties also provide information regarding the boundary condi-

tions of stellar evolution since they represent the final evolutionary state of ⇠95% of

stars. For example, the zero-age main-sequence masses of the progenitors range from

⇠0.8-8M� and white dwarf masses are typically 0.6M�, which means that mass loss

must play an important role during post-main-sequence evolution (Winget & Kepler

2008). The progenitor mass range is determined by the onset of helium core burn-

ing. Below 0.8M� the core never reaches a high enough temperature to ignite the

helium, and above 8M� the carbon will ignite to produce neon. The ability to mea-

sure accurate masses thus has an important role in the formation of stellar evolution

models.

The age of the Milky Way and its components can also be measured by studying

2.2. WHITE DWARF PROPERTIES 7

the properties of white dwarfs. This is because the evolution of a white dwarf consists

of only a cooling process, and this process is well understood (Fontaine, Brassard &

Bergeron 2001). Thus, the temperature of a white dwarf determines its age, and the

coolest white dwarfs represent the remnants of the oldest stellar populations in the

Milky Way (Winget et al. 1987). Using the white dwarf luminosity function and

cooling models, Leggett, Ruiz & Bergeron (1998) calculated the age of the Galactic

disk to be 8±1.5 Gyr. Hansen et al. (2002) used a similar method on white dwarfs in

globular clusters to estimate the age of the Galactic halo to be 12±1.5 Gyr. Kalirai

(2012) also used four field halo white dwarfs to estimate the age of the so-called ‘inner

halo’, that is the halo population closest to the Sun, and found it to have an age of

11.4±0.7 Gyr.

2.2 White Dwarf Properties

The importance of white dwarfs in the study of our Galaxy underscores the need

for accurate determination of their physical properties. This section presents the

results of previous studies of their various properties, such as mass, surface gravity,

temperatures, and spectral type. However, it begins by describing the formation

process and its e↵ect on these properties.

2.2.1 Formation and Composition

White dwarfs form at the end of the asymptotic giant branch (AGB) phase as the star

expels its shell leaving an exposed core (D’Antona & Mazzitelli, 1990). Three di↵erent

formation scenarios exist, with each scenario leading to a di↵erent core composition:


Table 2.1: Common white dwarf spectral types and their dominant characteristics.

Spectral Type Spectral CharacteristicsDA Only strong hydrogen absorption (Balmer Series)DB Only HeI absorptionDC No discernible features, continuous spectrumDO Strong HeII lines, also H lines presentDQ Carbon featuresDZ Metal lines only

1. The most common way to form a white dwarf is to eject the stellar envelope

during the planetary nebula phase. The progenitors are not massive enough

to fuse Carbon or Oxygen, and so the exposed core is composed of these two

elements. This method is the most dominant formation scenario.

2. A helium core can be produced if the mass loss of the progenitor is quick enough

to prevent helium burning. This would occur in progenitors with masses <1

M�. The resulting white dwarf is composed of the helium core which failed to

ignite.

3. The final scenario involves high mass progenitors for which the carbon core

ignites. The resulting white dwarf is composed of oxygen, magnesium, and

neon, which are products of the carbon burning process. Oxygen-Neon white

dwarfs are thought to be formed primarily in binary stars as the mass transfer

may be able to grow the progenitor to a high enough mass to ignite the core

(Willems & Kolb, 2004).


2.2.2 Spectral Types

White dwarfs also have di↵erent spectral types. The spectral type of a white dwarf

depends on its temperature and atmospheric thickness. A description of common

white dwarf spectral types can be seen in Table 2.1. The spectrum of typical DA, DB,

and DZ white dwarfs can be seen in Figure 2.2 with spectral features highlighted. The

spectral types are a result of the leftover elements from the cores of the progenitors.

The light elements dominate the atmospheres as a result of the strong gravitational

forces present, which allows them to ‘float’ to the surface (Kepler et al. 2016). This

process, called gravitational di↵usion, is very e�cient and occurs over a very short

time-frame (Fontaine & Michaud, 1979, Althaus et al. 2001).

While white dwarfs may come in many di↵erent spectral types, the majority of

the population are hydrogen rich and are thus classified as DAs. The Sloan Digital

Sky Survey (SDSS) (York et al. 2000) provided a perfect dataset to test the rela-

tive abundances of each spectral type, as it provided spectra for white dwarfs over

an immense area of the sky. Recent studies of white dwarfs (Kepler et al. 2015,

Kleinmann et al. 2013, 2015) show that roughly 80% of white dwarfs in the SDSS

are classified as a DA, 10% as a DB, 3% as a DC, 2% as a DZ, and 1% as a DQ.

The remaining fraction is composed of unknown type, a mix of hydrogen and helium,

or as a white dwarf with a main-sequence companion. The result is that DA white

dwarfs do indeed dominate the total fraction of the white dwarf population.

The spectral type of a white dwarf can also change throughout the cooling phase

(Bergeron et al. 1997). This evolution is dictated by the thickness of the atmosphere,

and is temperature dependent (Hansen & Liebert 2003). If a white dwarf has a thick


Figure 2.2: A typical spectrum for white dwarfs with a Hydrogen atmosphere (DA,top), Helium atmosphere (DB, bottom), and an atmosphere dominatedby metals (DZ, middle). Prominent spectral features are also highlighted,such as the Balmer Series, He I, and Ca H&K. Figure: Verbeek et al (2012)


atmosphere dominated by hydrogen it will remain a DA throughout the entirety of

the cooling process (Davis et al. 2009). However, white dwarfs with thin atmospheres

may change spectral type as they cool. At high temperatures (> 30 000 K) very few

DB white dwarfs exist (Eisenstein et al. 2006). A helium convection layer is formed

below 30 000 K which can dredge helium up to the surface. This can result in a DA

changing into a DB, and it is thought that up to 25% of DAs can undergo this change

(Hansen & Liebert 2003). A further change can occur below 5000 K as the hydrogen

and helium lines cease to be excited. The resulting spectrum becomes featureless and

is described as a DC (Davis et al. 2009).

Since gravitational di↵usion occurs on short time scales, the presence of heavy

metals in the atmospheres of DZ white dwarfs has always been a topic of interest.

Recently, these metals have been hypothesized to originate from the collision with

rocky debris disks which survived the post-main-sequence phase (Dufour et al. 2013).

These disks can be observed at infrared wavelengths, and have been discovered around

many DZ white dwarfs. These observations can provide information regarding the

composition of the leftover planetary systems (Dufour et al. 2013)

2.2.3 Physical Properties

The physical properties of a white dwarf can be calculated from its spectrum. This

section will detail the procedure to calculate various physical parameters, such as

mass, radius, temperature, and surface gravity, and will provide results based on

recent surveys.


Temperature and log g

Among all of the properties that can be derived for a white dwarf the temperature

and surface gravity (log g) provide the best description of the star (Althaus, 2010).

These properties can be derived using absorption line profiles. This is advantageous as

the line profile is highly dependent on these atmospheric parameters, and the physics

behind the profiles is well understood (see Tremblay & Bergeron 2009; Bergeron et

al. 1992; Barstow et al. 2003; Vennes et al. 2005; Schulz & Wegner 1981). The e↵ect

of the temperature and surface gravity on the line profile can be seen in Figure 2.3,

which uses the Balmer series as an example.

Figure 2.3 shows that the depth of the line increases at lower temperatures and

the width of the line increases with surface gravity. The broadening of the spectral

lines is a result of Stark (or pressure) broadening. This e↵ect occurs as a result of

collisions between charged particles within the white dwarfs atmosphere, and their

electric fields result in the spectral line being split. This process is stronger at higher

atmospheric temperatures and densities (surface gravity), and thus the observed line

profiles can be fit with model profiles which return predicted values for Teff

and log

g (Bergeron et al. 1992).

Kleinman et al. (2013) used spectral fitting to determine various parameters of the

known white dwarf sample within the Sloan Digital Sky Survey. The results can be

seen in Figure 2.4, and are sorted by type. The result is that the distribution of surface

gravities is very narrow and peaks at ⇠log g=8.0. The temperature distribution shows

the total number of white dwarfs increases towards lower temperatures. The number

of DB white dwarfs increases below 30 000 K and this is thought to be the result of


Figure 2.3: Theoretical line profiles for the Balmer series from H�

(bottom) to H8(top) at various temperatures and log g. The dashed line indicates logg=7.0 and the solid line is log g=9.0. Figure: Tremblay & Bergeron(2009).

the onset of a convective layer below the hydrogen layer which dredges up the helium

(Liebert et al. 2005; Koester & Kepler 2015).

Mass and Radius

The mass of a white dwarf can be measured in multiple ways, the easiest of which

is using its orbital parameters if it happens to be in a binary system. Since this is

not always the case, masses are estimated using white dwarf evolutionary models.

Liebert et al. (2005) used cooling models from Wood (1995) to derive a relationship

between log g and mass. A similar exercise was performed on the SDSS white dwarfs


Figure 2.4: Temperature (top) and log g (bottom) distributions for DA and DB whitedwarfs in the SDSS. The surface gravity is in units of cm/s2 Figure: Klein-mann et al. (2013)


Figure 2.5: Mass distribution for disk white dwarfs from Palomar Green Survey (Top)and SDSS DR7 (Bottom) Figures: Liebert et al. (2005); Kleinmann etal. (2013)

2.3. WHITE DWARF SURVEYS WITHIN THE VIRGO CLUSTERREGION 16

by Kleinman et al. (2013) using evolutionary models created by Renedo et al. (2010).

The results of both surveys can be seen in Figure 2.5, and both show that the distri-

bution of masses peaks at ⇠0.6M�. This result has been consistent throughout many

similar studies, and will be important for the model tracks used in this thesis.

One fundamental result which helps to convert the measured log g into a mass is

the use of the mass-radius relationship since the surface gravity is a function of both

the mass and radius. This relationship was first developed by Chandrasekhar in 1933

as a result of the degenerate state of the white dwarf and states that the radius scales

as ⇠M� 13 (Provencal et al. 1998).

This theoretical relationship can be tested independently by studying white dwarfs

in binary systems since their orbital parameters can provide mass and radius deter-

minations (Holberg 2012). Provencal et al. (1998) tested the theoretical mass-radius

relationships from Wood (1995) by calculating the mass and radius of 10 white dwarfs

in binary systems and 11 field white dwarfs with precise parallax measurements. The

results agreed with the theoretical predictions made by the Wood (1995) model and

further corroborated the white dwarf mass-radius relationship.

2.3 White Dwarf Surveys within the Virgo Cluster Region

This thesis uses data from the Next Generation Virgo Cluster Survey (NGVS) (Fer-

rarese et al. 2012) which is located at a high Galactic latitude (b⇠75�). This section

describes the largest UV/optical surveys which overlap with the sky coverage of the

NGVS. The survey will be introduced and explained in detail in Chapter 3.


2.3.1 The Sloan Digital Sky Survey

The Sloan Digital Sky survey (York et al. 2000) is a spectroscopic and photometric

survey which covers most of the Northern sky using a 2.5m Ritchey-Chretien telescope

at the Apache Point Observatory in New Mexico. Since its first data release in 2003

(Abazajian et al. 2003) the SDSS has provided an unparalleled combination of sky

coverage and depth in the Northern Hemisphere. The photometry is provided in

5 optical bands, ugriz, with 95% detection limits of 22.3/23.3/23.1/22.3/20.8 AB

magnitude and spectroscopy to approximately 18th magnitude in the g-band.

The SDSS has provided the largest and most extensive catalog of white dwarfs to

date. Using the first data release, Kleinman et al. (2004) discovered a total of 2551

white dwarfs. Most recently, Kepler et al. (2016) used Data Release 12 to increase

the total number of spectroscopically confirmed white dwarfs to over 25 000. The

methods employed to select white dwarfs in the SDSS use a combination of colour

cuts and spectral fitting (Eisenstein 2006). First, colour cuts are made in order to

select the bluest stars in the SDSS catalog since this region of colour-colour space

represents the hottest stars and separates the white dwarfs from the QSOs and main-

sequence stars. For example, Eisenstein (2006) used (g-r) < 0.2 and -2 < (u-g) <

0.833-0.667*(g-r) to select white dwarf candidates. Next, the selected spectra are fit

to calculate a temperature and surface gravity. The white dwarfs are then selected by

choosing objects with high surface gravity measurements (Kleinman et al. 2004). The

resulting catalogs are published and publicly available from CASJobs on the SDSS

servers.

The SDSS fully covers the Virgo Cluster region explored in this thesis. Querying


the SDSS database yielded 146 spectroscopically confirmed white dwarfs within the

NGVS field. These white dwarfs will be used as a test dataset to select white dwarf

candidates within the NGVS (see Chapter 3).

2.3.2 GALEX

The search for white dwarfs has also been done using the 50cm Galaxy Evolution

Explorer (GALEX) ultraviolet space telescope (Martin et al. 2005). GALEX covers

the ultraviolet portion of the electromagnetic spectrum from 135-280 nm. The original

GALEX mission consisted of a 40 000 deg2 All-Sky Imaging Survey (AIS), a 1000 deg2

Medium Imaging Survey (MIS), and an 80 deg2 Deep Imaging Survey (DIS) using two

bands: The Far-Ultraviolet (FUV) and the Near-Ultraviolet (NUV). These bands can

be seen in Figure 3.2 in Chapter 3. The 95% detection limits of the AIS/MIS/DIS

are 20.5/23.5/25.5 NUV AB magnitude respectively.

White dwarf selection using GALEX was done by Bianchi et al. (2011) by match-

ing AIS/MIS and SDSS photometry since GALEX lacked spectroscopic measure-

ments. Colour cuts of (FUV-NUV) < -0.13 were used to select the hottest stars in

the sample and an additional colour cut of (NUV-r) > 0.1 separates the stars from

QSOs. This method was aimed to select the hottest white dwarfs (Teff

� 18 000 K)

since they are brightest in the UV bands.

Within the NGVS field the resulting selection process yielded 175 white dwarf

candidates. However, the process makes no attempt to distinguish white dwarfs from

hot sub-dwarf stars and includes contamination (see Chapter 4.3 for more discus-

sion). Of the three contiguous GALEX surveys, only the AIS surveyed the Virgo

2.4. SELECTING HALO WHITE DWARFS 19

Cluster region. This means that the selected white dwarf candidates are brighter

than NUV⇠20.0 AB magnitude.

2.4 Selecting Halo White Dwarfs

2.4.1 Motivation

Despite their importance to Galactic formation and evolutionary models, halo white

dwarfs are one of the least studied populations of stars. This is due to their small

radii and faint optical luminosities. In the early 2000’s the population was considered

as a potential contributor to the dark-matter content in the Milky Way (ex. Oppen-

heimer et al. 2001), however, this view is now considered to be unlikely (Reid 2005).

However, despite the belief that the halo white dwarf population does not account for

a significant portion of the dark matter content, they can still provide information

regarding Galactic formation as well as stellar evolution models of Population II stars

(Rowell & Hambly 2011). Halo white dwarfs can also be used to estimate the age of

the halo by studying the white dwarf luminosity function and looking for the faintest

objects (Harris et al. 2006; Hansen et al. 1999).

The most extensive search for halo white dwarfs was performed by Rowell &

Hambly in 2011 using data from the SuperCOSMOS Sky Survey. This survey covered

nearly three quarters of the sky and obtained kinematic and photometric data for ⇠10

000 white dwarf candidates to a limiting magnitude of R ⇠ 19.75. Using a selection

tool of vt

> 200 km/s to select halo white dwarfs returned only 93 candidates, showing

that this population continues to be largely undetected at these optical magnitudes.

This thesis employs a similar method but uses data more than 5 magnitudes fainter

in the g-band.


Figure 2.6: The Galactic Space Velocity coordinate system. Figure: Carroll & Ostlie(2006).

2.4.2 Previous Results Using Kinematics

The ability to select halo white dwarfs relies on their kinematics, as opposed to

di↵erent spectral features which can be used to separate disk and halo main-sequence

stars. This is a result of gravitational di↵usion which allows the metals to sink below

the photosphere of the white dwarf (Cojocaru et al. 2015). The result is that disk

and halo white dwarfs can be separated using Galactic space velocities.

Galactic space velocities (U, V, W) are the velocities of a star in Galactocentric

coordinates. The geometry can be seen in Figure 2.6. The direction of the vectors in

Figure 2.6 represent the direction of positive velocities. Positive U velocities represent

motion away from the Galactic Center, positive V represents motion with the Galactic

Disk rotation, and positive W is towards the North Galactic Pole.


The Galactic space velocities can be used to determine a lower limit to the number

of halo objects in a kinematic sample of stars. This is because in these coordinates the

average space velocity of a pure halo population with respect to the local standard of

rest, that is the velocity which an object in the position of sun would travel if it were

in a perfectly circular orbit around the Galactic center, is (U,V,W) = (0, -220, 0)

km/s. This is a result of the lack of combined rotation in Galactic halo which yields

mean velocities of zero in U, V, and W. However, the halo population will have an

average V velocity of 220 km/s as a result of the rotation of Sun about the Galactic

Center.

This method has been used multiple times in order to determine whether the

halo contributes to the local white dwarf population. These white dwarfs would

have high space velocities, but their orbits happen to currently place them within

the solar neighborhood. An example of such work includes Kawka & Vennes (2006),

who used a catalog of high proper motion white dwarf candidates from the revised

New Luyten Two-Tenths catalog (Salim & Gould 2003). They calculated distances

using colour-absolute magnitude relations, and U and V velocities from the measured

proper motions. They over-plotted typical velocity ellipsoids from the thin disk (solid

ellipse), the thick disk (dashed ellipse), and the halo (dotted ellipse) from Chiba

& Beers (2000) in order to determine which Galactic component each white dwarf

belonged to. This method returned zero halo white dwarfs and suggested that every

white dwarf within their sample belonged to the disk. Their results can be seen in the

top panel of Figure 2.7. This result has also been corroborated by Sion et al. (2014)

who used a sample of white dwarfs within 25 pc and found no compelling evidence

that this sample included a halo white dwarf.


Figure 2.7: U and V velocities in km/s for white dwarfs from (Top) Kawka & Vennes(2006) and (Bottom) Oppenheimer et al. (2001). The velocity ellipsoidsfor a thin disk, a thick disk, and a halo population are plotted where thedisk components are centered at (U,V) = (0,0) and the halo is at (-220,0). The squares represent selected halo candidates. Figure: (Top) Kawka& Vennes (2006), (Bottom) Oppenheimer et al. (2001).


The largest survey used to characterize halo white dwarfs was done by Oppen-

heimer et al (2001) using the SuperCOSMOS Sky Survey. This survey covered ⇠4000

deg2 at the Southern Galactic Cap and provided photometry and proper motions for

stars with R < 19.8. Since the survey was at high Galactic latitude the U and V

velocities could be described solely from the proper motions in RA and DEC. Using

the U and V velocities Oppenheimer (2001) selected 38 stars with halo-like velocities,

and these candidates can be seen in the bottom panel of Figure 2.7. The work was

corroborated by Salim et al. (2004) who improved the photometric and astrometric

measurements for the sample.

The common theme throughout the studies which attempt to identify halo white

dwarfs is that they are rare. The studies presented above found very few candidates,

particularly Rowell & Hambly (2011) who identified less than 100 potential halo can-

didates out of their sample of ⇠10 000 white dwarfs. This thesis will use photometric

data which is more than 5 magnitudes fainter than the Oppenheimer et al. (2001)

study in order to determine whether a sample of halo white dwarfs can be identified.

Various methods will be presented, including tangential velocities, U-V velocities, and

photometric distances.

2.4.3 Globular Clusters and the Inner Halo

Globular clusters o↵er a unique study of population II because they allow for a study

of many stars at once, all of which are at similar distances, ages, and compositions.

Using the distances an accurate HR diagram can be created, which allows for a

comprehensive study of their stellar population. Since they contain some of the

oldest stars in the Milky Way, the population is composed of low-mass stars in the


late stages of their evolution, as well as the remnants of the intermediate mass stars.

This allows for a large study of white dwarfs at di↵erent positions along the cooling

sequence.

Kalirai et al. (2009) used ultra-deep Hubble Space Telescope (HST) imaging to

study the white dwarf cooling sequence of Messier 4 (M4). The study revealed over

2000 white dwarfs within the cluster. M4 has an estimated age of 12.5 Gyr and lies

at a distance of 2.2 kpc in the direction of the Scorpius constellation (Kalirai 2012).

The colour-magnitude diagram derived from the HST imaging can be seen in Figure

2.8.

The circled region in Figure 2.8 displays the location of the hottest white dwarfs

in M4. These also represent the most recently formed white dwarfs in the cluster.

Kalirai (2012) used the Keck Telescope to obtain spectroscopic measurements for six

of these newly-formed white dwarfs, as well as four white dwarfs from Pauli et al.

(2006) which had halo-like Galactic space velocities. Fitting the spectra of the white

dwarfs from Pauli et al. (2006) revealed temperatures between 14 000 K and 20 000

K, suggesting that they formed between 25-300 million years go. These white dwarfs

are similar to the six recently formed white dwarfs from M4, which had cooling ages

of approximately 100 Myr.


Figure 2.8: Colour-Magnitude diagram for the globular cluster M4 using HST filters.The circled region represents recently formed white dwarfs. Figure: Kali-rai (2012)

26

Chapter 3

The Data: Constructing an Optical-Ultraviolet

Catalog

This chapter details the data sets used as part of this thesis and how they were used

to construct a joint optical-UV catalog. An overview of the acquisition, telescopes,

and instruments are presented, including relevant parameters associated with the final

catalogs.

3.1 The Next Generation Virgo Cluster Survey

The optical data used in this thesis was obtained as part of the Next Generation

Virgo Cluster Survey (NGVS) (Ferrarese et al. 2012). The NGVS is a deep optical

multi-band survey of the Virgo Cluster. The footprint covers 104 deg2 centered on

M87 (l=283.78, b=74.49) and extends to the virial radius of both the Virgo A and B

substructures. The footprint of the survey can be seen in Figure 3.1.

The NGVS was acquired using Megacam, which is a high-resolution imaging cam-

era mounted at the prime focus of the 3.6m Canada-France-Hawaii telescope. Mega-

cam is ideal for this type of survey as it provides a large field of view with uniform

3.1. THE NEXT GENERATION VIRGO CLUSTER SURVEY 27

Figure 3.1: The Next Generation Virgo Cluster Survey (NGVS) field in J2000 equato-rial coordinates. The yellow lines correspond to a single Megacam point-ing, and the grid totals 117 separate pointings. The background is com-posed of real NGVS images and was taken from the NGVS graphicalsearch tool.


Figure 3.2: The transmission curves for the filters used in the NGVS with a typicalDA white dwarf spectrum for reference (black).

image quality and also has good sensitivity in the near-UV. Megacam is composed of

36 CCDs, each measuring 2048 x 4612 pixels, and has a field of view of ⇠0.90 deg2.

The NGVS was carried out in 3 optical (u*, g’, r’) and 2 near infrared (i’ and

z’) bands resulting in wavelength coverage from ⇠ 380-1000 nm. The transmission

curves can be seen in Figure 3.2 with the spectrum of a DA white dwarf overlaid to

show how the filters line up with its spectral features. The truncation of the white

dwarf spectrum at ⇠3800 A is a result of the limits of the SDSS u-band. These filters

are similar, but not identical, to filters used in the Sloan Digital Sky Survey (York et


Table 3.1: Various parameters for the NGVS including the exposure times, magnitudelimits, seeing, and background constraints.

FilterExposureTimes (s)

NGVS PointSource Limits(mag)

SDSS PointSource Limits@ 95% detection(mag)

MedianFWHM(arcsec)

MoonIllumination

u⇤ 11 x 582 26.3 (S/N = 5) 22.3 0.88 10%g 5 x 634 25.9 (S/N = 10) 23.3 0.80 10%r 7 x 687 25.3 (S/N = 10) 23.1 1.0 40%i 5 x 411 25.1 (S/N = 10) 22.3 0.54 anyz 8 x 550 24.8 (S/N = 5) 20.8 0.75 any

al. 2000), however, magnitudes measured in one filter set can be transformed using

relationships described in Ferrarese et al. (2012). All optical magnitudes quoted in

this thesis will be in the Megacam system unless otherwise noted.

This thesis will exploit the high image quality, depth, and seeing obtained as

part of the NGVS. The resolution of an image is described by the full-width half

max (FWHM) of the point spread function (PSF) of an unresolved source, such as a

star. During the NGVS acquisition the FWHM in all bands never exceeded 1”, and

is particularly sharp in the g’ and i’-band with median FWHMs of 0.77” and 0.52”

respectively. This leads to very precise astrometric positions, which are crucial for

deriving proper motions (see Chapter 5).

Table 3.1 shows the exposure times achieved in the NGVS. One important aspect

of the project was that the final dataset was created by stacking images. This practice

is used to increase the signal-to-noise by creating the equivalent of a long exposure

image by adding the flux from multiple short exposures. The total number of images,

and the exposure time of each image, are listed in column 2. Once the images have

been stacked, SExtractor (Bertin & Arnouts 1996) was used to extract a catalog of


objects. The sub-exposures were also dithered, which is a process that shifts the

pointing of the images, to allow for the removal of cosmic rays and to fill in the gaps

between images.

The depth achieved in the NGVS is instrumental to detecting white dwarfs as

they are faint in the optical region of the spectrum. The point-source magnitude

limits of the NGVS survey can be seen in Table 3.1. These limits are also compared

with SDSS and show that the NGVS detects sources that are >2 magnitudes fainter.

However, when the surveys are compared at equal signal-to-noise limits this di↵erence

is even larger. The u and g-band images were also taken under the darkest sky

conditions (10% moon illumination) in order to minimize the background light

detected, however, the i and z images were taken under any sky conditions as the sky

is not as bright in the near-infrared.

3.1.1 Extracting Point Sources

After the SExtractor was run on the processed stacked images, a catalog of sources

was created. This catalog included every object observed in the NGVS including

galaxies, stars, and QSOs. The white dwarfs, as well as any stellar object, detected

in this survey are at a large enough distance that they will be detected as point

sources. Thus, in order to decrease contamination from resolved sources, the point

sources must be extracted from the master catalog.

The point source extraction was done in accordance with Durrell et al. (2014)

who used the NGVS to study the spatial distribution of globular clusters in the Virgo

Cluster. At the distance of the Virgo Cluster many of the globular clusters were

expected to be point sources (Durrell et al. 2014). In order to extract point sources,


Figure 3.3: The concentration index for NGVS Sources (grey) and stellar objects fromSDSS (black). A sharp stellar sequence centered on �i = 0, and a clusterof background galaxies can be seen towards fainter g-band magnitudes.Only 100,000 point sources from the NGVS are plotted for clarity. Thedashed lines indicate the chosen point source range used in this thesis andis in agreement with Durrell et al. (2014).

the authors used the concentration index, �i, which is the di↵erence between the

4-pixel and 8-pixel aperture i-band magnitudes of an object. These magnitudes were

calculated by summing all of the flux within a specified aperture. Since each pixel

covers 0.187” on the sky, the 4-pixel diameter of 0.75” should cover most of the

flux within the seeing disk. When the aperture over a point-source is doubled there


will only be a negligible amount of flux added as a result of the residual light left

over from the background subtraction. This results in concentration values above

zero. A negative concentration index occurs when the background subtraction is

overestimated. The result is that for a true point source the concentration index

should be roughly zero. The i-band was used to calculate the concentration index of

an object because it was taken under the best seeing conditions. The resultant PSF

of objects in the i-band is more concentrated than in any other band obtained in the

NGVS.

In order to quantify the range of concentration index needed to extract point

sources, a set of known point sources was queried from the SDSS. These objects were

all spectroscopically classified as stars, and should thus be point-sources. The result-

ing plot of concentration index versus g-band magnitude can be seen in Figure 3.3,

where the black triangles represent the stars from the SDSS. By visually inspecting

Figure 3.3 a concentration range of

�0.1 < (i4 � i8) < +0.15 (3.1)

was chosen, where i4 and i8 are the 4-pixel and 8-pixel i-band magnitudes respec-

tively. The chosen range can be seen as the dashed lines in Figure 3.3, and is in

agreement with Durrell et al. (2014).

Applying the concentration criterion to all NGVS point sources leads to a catalog

of 5,345,653 objects. This catalog will be referred to as the NGVS point-source catalog

for the remainder of this thesis.

3.2. THE GALEX ULTRAVIOLET VIRGO CLUSTER SURVEY 33

3.2 The GALEX Ultraviolet Virgo Cluster Survey

The ultraviolet catalog used in this thesis was constructed as part of the GALEX

Ultraviolet Virgo Cluster Survey (GUViCS) (Voyer et al. 2014). The catalog is a

combination of archival data from the original GALEX surveys (AIS, MIS, DIS), the

Nearby Galaxies Survey (NGS), as well as PI programs. The Virgo Cluster region

was covered mainly by the AIS in the original GALEX survey. In order to improve

the depth of the UV data, the GUViCS team was awarded time in 2010 to cover the

NGVS footprint to a depth equivalent to the MIS. GUViCS covers an area of ⇠120

deg2 centered on M87 and covers most of the NGVS field. Some regions were avoided

due to the presence of bright stars in order to prevent saturation of the detectors.

The sky coverage can be seen in Figure 3.4, with the NGVS field indicated in yellow.

The GUViCS survey contains UV photometry obtained from the GALEX satellite

in both the near-ultraviolet (�eff

= 2316 A) and far-ultraviolet (�eff

= 1539 A) bands,

and is a combination of multiple observations. In order to maximize the point-source

depth, only the detection with the highest signal-to-noise for a given object was

retained. The resulting catalog contains NUV data to a depth of mNUV

= 23.1, and

FUV data to a depth of mFUV

= 19.9. The ability to take data in the FUV was lost in

early 2010 and was therefore unavailable during the GUViCS follow-up observations.

This thesis will therefore focus on the deeper NUV data in order to best match the

depth of the NGVS.

An important feature of the UV data is the FWHM of the PSF. Since the GALEX

telescope was operated from space it had the advantage that the PSF is dominated

by the properties of the telescope as opposed to the turbulence in the atmosphere.

3.2. THE GALEX ULTRAVIOLET VIRGO CLUSTER SURVEY 34

Figure 3.4: The sky coverage for the GUViCS catalog is shown in RA and DEC(J2000) compared to the NGVS footprint (yellow). The dashed circlesrepresent the targeted GUViCS pointings, and the colour indicates theexposure time of the image. The red circles indicate exposure times be-tween 800 and 1500 seconds, the cyan circles represent images with MISdepth (exposure time ⇠1500-30 000 seconds), the dark blue circles indi-cate images with exposure times greater than 30 000s. Black areas rep-resent fields that were avoided due to the presence of a bright foregroundstar. As a reference, the fields from the far-infrared survey HeViCS areshown in green. Figure: Voyer et al. (2014).

3.3. MATCHING THE CATALOGS 35

The PSF in the NUV band has a FWHM of ⇠4-6 arcseconds, with a median of 5.3

arcseconds (Bianchi et al. 2014). The resolution of GALEX is dominated by the

microchannel plate detector, which has 1.5 arcsecond pixels, as well as the telescope

optics and the ground pipeline (Martin et al. 2003; Morrissey et al. 2007; Llebaria

et al. 2008). This resolution was tolerable for the science goals of GALEX since they

were extragalactic in nature.

A point-source catalog was created and published by Voyer et al. (2014). The

catalog consists of 1 231 331 sources, of which 706 847 fall within the NGVS footprint.

This catalog will serve as the ultraviolet source catalog for the remainder of this thesis.

3.2.1 Caveats

A feature of the GUViCS catalog is that during the creation of the point-source

catalog the authors removed 12 211 bright foreground stars which appeared in the

SIMBAD database. This was done because the main science drivers of the catalog

were extragalactic in nature. Since this thesis is focused on the ability to select

stars, the catalog was combined with the hot star catalog of Bianchi et al. (2011) to

supplement the GUViCS data.

3.3 Matching the Catalogs

In order to create a unified UV-optical catalog for sources in the NGVS field, the

NGVS and GUViCS catalogs were matched. This section details the matching proce-

dure, as well as what was done in the event of a multiple match. A discussion about

spurious matches is also presented.


3.3.1 Creating a UV-Optical Catalog

The UV and optical catalogs were matched using TOPCAT (Taylor 2005), which

is software designed specifically to match astronomical catalogs. TOPCAT merges

the catalogs using their equatorial coordinates (RA and DEC) and matches objects

within a specified search radius. If there happen to be multiple objects within the

specified search radius, TOPCAT will designate all of the objects in the search radius

as a match and place them in a unique group. TOPCAT returns all objects which

have at least one match between the NGVS and GUViCS catalogs in a list.

The search radius chosen in this thesis was 3”. This was chosen to coincide with

work done by Bianchi et al. (2011) who matched GALEX and SDSS data. The 3”

radius was chosen to match the large PSF of the GALEX observations. Applying

the chosen search radius to the NGVS-GUViCS catalogs results in 110 755 unique

UV sources with at least one optical counterpart and 117 590 unique optical sources

with at least one UV counterpart. Removing the multiple matches leads to 104 050

one-to-one matches, which will be used as the NGVS-GUViCS matched catalog for

the remainder of this analysis.

3.3.2 Multiple Matches

The depth of the NGVS combined with the large search radius means that there will

be instances of multiple matches. When matching the two catalogs, there are two

scenarios where a multiple match can occur. The first, and dominant case is multiple

optical counterparts being attributed to a single GUViCS source. The second case

is multiple GUViCS sources being attributed to a single NGVS source, however, this

case is rare. The objects with multiple matches are discarded since the broad point


spread function of the UV object means that it may be a superposition of all possible

optical counterparts within 3 arcsec. This would render meaningless colours and

consequently these instances were removed (Bianchi et al. 2011). A discussion on the

impact of this decision is presented in Chapter 7.

3.3.3 Spurious Matches

The high spatial density of the optical data means that there is a possibility that two

unique sources may be matched together. This case is termed a spurious match, and

can happen as a result of the di↵erent wavelength regimes probed in the two surveys.

For example, a faint QSO and a late-type star may lie within the match radius of a

UV source. When viewed in the UV, the QSO would be detected, however, the QSO

could be too faint in the optical to be detected. The opposite is true for late-type

stars (such as an M-dwarf). This could lead to the UV detection of the QSO being

matched to the optical detection of the late-type star.

The likelihood of a spurious match was estimated in two ways. First, a collection

of 1 square degree cutouts from the GUViCS sample were selected, and 1 degree was

added to both their RA and DEC. The resulted tessellated cutout was then matched

to the NGVS catalog and the spurious match rate was calculated as the total matches

divided by the total number of points within the square degree o↵set. Repeating the

exercise for 5 di↵erent pointings led to a spurious match rate of ⇠2%

A second method to estimate the spurious match rate was done using a Monte

Carlo method. A 0.5 deg2 field of both GUViCS and NGVS data was taken in order

to represent the spatial density of both catalogs. The NGVS data was then replaced

by an equal number of randomly generated coordinates using the random.uniform


function in Python. A nearest neighbors algorithm was applied to the resulting

coordinates in order to determine the distance to the three nearest mock optical

objects for each GUViCS object. If the distance to one of the three nearest neighbors

was less than three arcseconds then it was considered to be a match. This exercise

was repeated 100 times for each GUViCS point. The calculated average spurious

match rate was ⇠3%.

Both methods show that the contamination from spurious results is approximately

2-3%. However, the resulting colours from spurious matches could be very odd de-

pending on the types and magnitudes of the two objects. For this reason, a spurious

match is not expected to contaminate the results presented in this thesis.

39

Chapter 4

White Dwarf Candidates within the NGVS Field

This chapter describes the process used to select white dwarf candidates within the

NGVS field using the matched NGVS-GUViCS catalog created in Chapter 3. The

selection process using colour cuts is described in Section 4.1, while the possible

contaminants are discussed in Section 4.2. Finally, a comparison to previous UV-

optical selections methods is presented in Section 4.3.

4.1 Selection Procedure

4.1.1 Choosing Colours

A common way to separate di↵erent classes of astronomical objects is to use their

colours. Di↵erent types of objects will have di↵erent colours depending on their

spectral energy distribution. For example, Wien’s Law,

�max

=b

T(4.1)

4.1. SELECTION PROCEDURE 40

where b = 2.8977685 x 10�3 m K is a constant, shows that hot stars (T>10 000 K)

will have a peak wavelength in the NUV band (290 nm). The resulting spectral energy

distribution dictates that hot stars will brighter in the NUV band than in an optical

band. The (NUV-g) colour will thus be numerically smaller, as numerically larger

apparent magnitudes represent fainter objects. On the other end, cool stars will peak

in the near-infrared, such as the i-band, and will be fainter in the NUV band. This

will lead to a positive (NUV-g). This method will be used in the following analysis

to separate newly formed white dwarfs, which have high temperatures, from other

objects such as main-sequence stars and QSOs.

First, a collection of spectroscopically confirmed white dwarfs, stars and QSOs

from the SDSS were compiled. The white dwarfs were taken from Kleinman et al.

(2015), the QSOs from Paris et al. (2014), and the stars were queried from SDSS

DR12. Another class of objects called blue horizontal branch (BHB) stars and blue

stragglers (BS) stars were taken from Scibelli et al. (2014). Blue horizontal branch

stars are post-main-sequence stars which turn blue-ward on the Hertzsprung-Russell

diagram after the onset of helium core burning. Blue stragglers are hot stars which

stay on the main-sequence longer than predicted. Blue stragglers are thus bluer than

the main-sequence turn-o↵ point. Blue stragglers and blue horizontal branch stars are

included due to their blue colours, as they may contaminate the white dwarf selection

(see Chapter 3.2).

The ability to select the white dwarfs is critically dependent on the capability to

separate the di↵erent classes of astronomical objects. In order to see the value of the

NUV filter, consider Figure 4.1, which shows a colour-colour diagram composed of

strictly Megacam u*, g, and i filters. This colour-colour diagram shows that many


Figure 4.1: Colour-colour diagrams of spectroscopically confirmed objects from theSDSS that have been matched to the NGVS. The blue points indicatewhite dwarfs from Kleinmann et al. (2015), the red points are QSOs fromParis et al. (2014), the cyan points are blue stragglers and blue horizontalbranch stars from Scibelli et al. (2014), and the stars were queried usingthe SQL search in SDSS DR12. The separation between the objects ispoor when compared to the (g-i), (NUV-g) diagram in Figure 4.2.

of the spectroscopically confirmed white dwarfs (blue points) overlap with either the

QSOs (red), the main sequence stars (magenta), or the blue horizontal-branch stars

(black). Specifically, the main-sequence stars and BHB/BS stars occupy the same

region of colour-colour space as many of the redder white dwarfs.

After trying many di↵erent colour-colour combinations it was determined that


Figure 4.2: (g - i), (NUV - g) colour-colour diagram for the matched NGVS-GUViCSpoint source objects (grey), spectroscopically confirmed SDSS WDs (bluecircles), QSOs (red squares), and stars (magenta triangles). The blackdashed lines indicate the chosen colour cuts applied to the NGVS-GUViCS data in order to select white dwarf candidates while minimizingcontamination from other objects.

the (g-i), (NUV-g) colour-colour diagram provided the best separation between the

white dwarfs, BHBs, QSOs, and main sequence stars. The resulting plot can be seen

in Figure 4.2. The dashed lines in Figure 4.2 show the location of the colour cuts

applied to the NGVS-GUViCS catalog to select white dwarf candidates. These lines

were selected in order to include as many confirmed white dwarfs, whilst minimizing

the contamination from other objects.


The resulting colour cuts were chosen by visually inspecting the locations of the

spectroscopically confirmed objects in the (g - i), (NUV - g) plane with the goal of

minimizing contamination from QSOs and hot sub-dwarfs (see section 4.2 for further

discussion). A magnitude cut of g < 24.5 was also imposed in order to minimize

the contamination by objects with large photometric errors. The black dashed lines

in Figure 4.2 & 4.3 show the location of the colour cuts used to select white dwarf

candidates. First, all objects with (g - i) < -0.3 are selected, which represents the

bluest objects in the data set. The second cut is indicated by the diagonal dashed

lines and is used to separate the bluest main sequence and sub-dwarf stars from the

white dwarfs. A further cut is made on the UV data by only selecting objects with

uncertainties below 0.3 mag. Applying these colour cuts results in the selection of

852 white dwarf candidates.

In order to confirm the selected region, a series of model evolutionary tracks for

white dwarfs, stars, and QSOs was plotted in the same colour-colour plane. The

models originate from Bianchi et al (2009) and were modified to fit the Megacam

and GALEX filters used in this thesis. The white dwarf model is derived from the

TLUSTY model (Hubeny & Lanz, 1995) and the main sequence stars are modeled

using a Kurucz model (Kurucz, 1993) which produces model absolute magnitudes as

a function of temperature and log g. The QSO model is a function of redshift, and

was provided by Luciana Bianchi (see Bianchi et al. 2009, 2011). The location of the

models in colour-colour space can be seen in Figure 4.3. Comparing Figure 4.2 with

Figure 4.3 shows that the models and observations are in good agreement.

A further addition to the white dwarf candidates was made using the results from

Bianchi et al. (2011). This is required because the GUViCS catalog removed many


Figure 4.3: Model tracks from Bianchi et al. (2009a) are plotted to show ideal lo-cations for the WDs (blue), QSOs (red), and for stars of various surfacegravities (magenta). The WD track is for a log g = 8.0 for temperaturesbetween 15,000 and 200,000 K. The model QSO colours are a function ofredshift, with values between 0 and 5. Model stars with log g = 5, 4, 3are indicated by the solid, dashed, and dotted magenta lines, respectively.The black dashed lines indicate the chosen colour cuts applied to selectwhite dwarf candidates.

bright stars during the creation of their point-source catalog. A further 42 candidates

were added by applying the same colour cuts to the shallower GALEX AIS data,

bringing the total number of candidates up to 894, of which 52 are spectroscopically

confirmed by the SDSS.

4.2. CONTAMINANTS 45

4.2 Contaminants

As Figure 4.2 shows, objects that are not white dwarfs can fall within the same

region of the g-i, NUV-g plane as a white dwarf. The main source of contamination

in the bluest regime are O sub-dwarfs (sdO), while towards redder colour, the main

contamination is from QSOs with high UV fluxes. sdO stars are thought to be the

cores of red giant stars which ejected their surrounding shell prior to reaching the

AGB-phase (Geier 2006). They are also very blue, however, they are very rare and

not expected to strongly contaminate the white dwarf candidates.

The contamination rate for QSOs was estimated by matching the white dwarf

candidates to the NGVS Master Spectroscopic Catalog (MSC), which is a compilation

of all available spectroscopic redshifts for objects within the NGVS footprint. The

catalogs included within the MSC include the SDSS DR12, NED, and VCC, as well

as a number of NGVS PI programs that targeted the Virgo Cluster with a variety of

telescopes including KEck, AAT, and MMT. Matching the white dwarf candidates to

the MSC returns 66 matches, of which 60 are classified as stars and 6 are classified

as extragalactic. The extragalactic sources are all located in the red portion of the

selection region.

In order to quantify the contamination by sdO and other hot stars, the white

dwarf candidates were matched to SDSS DR12. Matching the two catalogs returned

59 matches, of which 52 were classified as WDs and 7 were classified as di↵erent types

of hot stars. These stars are all bright (brighter than g⇠18) and therefore will not

a↵ect the selection of halo white dwarfs performed in Chapter 5 as these are much

fainter.

4.3. COMPARISON TO SDSS-GALEX AIS MATCHING 46

4.3 Comparison to SDSS-GALEX AIS Matching

A similar exercise was performed by Bianchi et al. (2011), for which GALEX data

release 5 was matched with SDSS DR7. The authors used a colour cut of (FUV -

NUV) < 0.13 to select hot stars, and (NUV - r) < 0.1 to separate the single star

candidates from the binary candidates (see Fig 5). This selection method di↵ers from

the one presented in this thesis for two reasons. First, the r-band data for the NGVS

was never completed over the whole field (Raichoor et al. 2014). Second, the GALEX

FUV detector was out of commission when the GUViCS data was taken, and thus

the data is shallow and incomplete over the NGVS field.

One of the main downfalls with the SDSS-GALEX matching is that some of the

hottest and faintest white dwarfs detected in the UV will not appear in SDSS (Bianchi

et al. 2011). At the magnitude limit of the GUViCS data (NUV ⇠ 23), which is

equivalent to the MIS data used in the SDSS-GALEX matching, Figure 4.3 shows

that the hottest WDs have typical (NUV - g) = -1.5. This means that optical data

must be complete to g ⇠ 24.5 to allow for the detection of the hottest white dwarfs.

Since the depth of SDSS is only 23.1 in the g-band, many of these hot white dwarfs

would not have been detected. By contrast, the depth of the NGVS optical data (g

⇠ 26) means that all of the white dwarfs detected in the GUViCS data should also

appear in the NGVS data.

The ability to recover sources that were selected by Bianchi et al. (2011) provides

a quick check of the colour selections used in this analysis. Of the 28 319 classified hot

star candidates from Bianchi et al. (2011), 179 fall within the NGVS footprint. Of

the 179 sources, 126 fall within the region of colour-colour space used to select white

4.3. COMPARISON TO SDSS-GALEX AIS MATCHING 47

dwarfs detailed in Figure 4.3. Matching these 126 objects to the catalog obtained

in Chapter 3.1 returns 84 sources. The remaining 42 are lost because the GUViCS

catalog removed all bright foreground stars that were matched to the SIMBAD catalog

as discussed in Chapter 3. These objects do appear in the NGVS catalog and were

appended to the list of white dwarf candidates.

This check shows that all objects classified as white dwarfs in the shallower

GALEX-SDSS matching are also classified as white dwarfs in this thesis. The number

also show that the SDSS-GALEX matching was not su�cient to select white dwarfs

below g⇠ 21 as a result of the shallow NUV data. Also, even with the deeper NUV

data provided by GUViCS, the shallower optical data acquired by the SDSS would

not detect some of the hottest white dwarfs due to their faint optical luminosities.

Thus, the combination of deep optical data from the NGVS and the NUV data from

GUViCS will return the highest number of white dwarfs in this Galactic region.

48

Chapter 5

Photometric Properties

This chapter presents various properties of the white dwarf candidates extracted in

Chapter 4 including magnitude distributions (Section 5.1), photometric distances

(Section 5.2) and proper motions (Section 5.3).

5.1 Magnitude Distributions

The first photometric property that will be explored is the distribution of magnitudes

in the GALEX NUV, Megacam g, and Megacam i bands for the white dwarf candi-

dates. These distributions can be seen in Figure 5.1, where they are compared to the

photometric selections made in the Bianchi et al. (2011) using GALEX-SDSS match-

ing (B11), and the SDSS spectroscopic selections made by Kleinman et al. (2013)

(K13). Figure 5.1 highlights the depth of both the NGVS and GUViCS data, as the

candidates selected in this work agree with previous spectroscopic and photometric

classifications to a depth of g⇠19.0. Below g⇠19.0 the selection method described in

Chapter 4 selects more than five times the number of previously discovered objects.

The distribution also peaks at g⇠21.5, which is below the spectroscopic threshold of

the SDSS and the photometric limit of the GALEX AIS.

5.1. MAGNITUDE DISTRIBUTIONS 49

Figure 5.1: Magnitude distributions for the white dwarf candidates selected in Chap-ter 4 compared to the SDSS spectroscopic selection from Kleinman et al.2013 (K13) and GALEX AIS photometric selection from Bianchi et al.(2011) (B11).


5.1.1 Model Comparison

TRILEGAL is a stellar population synthesis code developed by Girardi et al. (2005)

which generates a mock photometric catalog along a chosen line-of-sight based on

input model evolutionary tracks. TRILEGAL has the ability to simulate surveys

in a variety of photometric bands, which makes it a particularly useful model when

comparing matched catalogs that contain di↵erent photometric systems.

A mock catalog was created in order to compare with results obtained using

the selection criteria in Chapter 4. The mock catalog was centered on the galactic

coordinates of M87, which is approximately the center of the NGVS field. The total

area of the catalog was selected to be largest the model will allow, 10 deg2, in order

to best match the size of the NGVS footprint. A limiting magnitude of 26 in the

g-band was chosen to also match the NGVS. All other input parameters were left at

the default option, including a Chabrier IMF, a Milky Way extinction model, and a

three component Galactic model including a squared hyperbolic secant thin and thick

disk and an oblate halo. Due to the high Galactic latitude of the NGVS, the field

will not include any bulge stars and so bulge parameters were not included.

The resulting catalog contains various parameters for the mock stars including

apparent magnitudes in the five Megacam bands, temperature, log g, and mass. White

dwarfs were selected based on their log g, and any mock star with a log g>7.5 was

considered to be a white dwarf. A magnitude cut was also imposed on the (g-i) <

-0.3 color to match the selection criteria that was imposed on the NGVS data.

The resulting magnitude distribution can be seen in Figure 5.2. The data has been

binned in 0.5 magnitude bins and displayed as a surface density, instead of a total


Figure 5.2: g-band magnitude distribution for the white dwarf candidates (green) andthe TRILEGAL white dwarfs (white).

count, in order to account for the smaller area of the mock catalog. The resulting

surface densities of the mock catalog follow the same distribution as the real data

to a depth of g=22.5, however, the mock data consistently over-predicts the surface

density throughout this regime. Below g=22.5 the mock data over-predicts the data

by a factor of 10 or more.

The magnitude distribution in the g-band su↵ers from incompleteness below g⇠22

as a result of the completeness limit of the GUViCS survey (NUV⇠23.1). The white


dwarfs selected in Chapter 4 have typical (NUV-g) colours between -1 and 1 (see

Figure 4.2), with numerically higher values indicating lower temperatures. This means

that the cooler white dwarfs with (NUV-g)=1 are lost below g⇠22.1.

Three other scenarios exist that could explain an over-prediction by the model.

First, some UV candidates were lost as a result of being matched to multiple optical

sources. As discussed previously, these objects were removed since the GUViCS

PSF could potentially be a superposition of the optical sources within the search

radius. This would lower the total number of candidates, and could explain the

discrepancies between the model and the real data. A second scenario which could

explain the over-prediction is the cut in the (NUV-g). The TRILEGAL model is

not currently equipped to generate a mock catalog with both Megacam and GALEX

filters simultaneously and thus a corresponding (NUV-g) cut which matched the one

presented in Chapter 4 could not be imposed. This cut could potentially lower the

total number of objects predicted by the model. The final scenario involves the

uncertainties in the UV data. The GUViCS catalog contains objects with NUV

magnitude uncertainties as large as 0.3 mag. By imposing a magnitude cut on the

NGVS-GUViCS data some of the white dwarfs with larger uncertainties may not fall

within the selected region shown in Figure 4.2.

Large discrepancies at faint magnitudes have also been found by Bianchi et al.

(2009, 2011). The authors suggest that the reason for the over-prediction is caused

by the chosen initial-final mass relation (IFMR). This relation, despite it being very

important to our understanding of stellar evolution, is not well constrained (Bianchi

et al. 2011). The IFMR determines the mass of the white dwarf based on the initial

mass of the progenitor. White dwarfs have a very well constrained mass regime,

5.2. PHOTOMETRIC DISTANCES 53

⇠0.4-1.0M�, however, the progenitor masses vary from ⇠0.8-8.0M� meaning that a

small change in white dwarf mass has a large influence on the inferred progenitor

mass (Bianchi et al. 2011). Since the progenitor mass influences the total lifetime of

a star it will also a↵ect the total number of white dwarfs which are produced.

5.2 Photometric Distances

The ability to determine the distance to a star is essential in order to study its

kinematics. As Figure 5.1 shows, the white dwarf candidates in this work are all

fainter than g⇠18. Hipparcos (ESA 1997) obtained parallax measurements for stars

brighter than V⇠12, and so parallax measurements would not have been acquired

for the white dwarf candidates (Perryman et al. 1997). In the absence of parallax

measurements, an estimate of the distance to these candidates can be made using

theoretical colour-magnitude relations.

Distances to a subset of candidates were made using the TLUSTY model (Hubeny

& Lanz 1995) cooling curve seen in Figure 4.3. This model returns absolute magni-

tudes for a given temperature and surface gravity. The end result is that a relation-

ship between the (g-i) colour and the absolute g-band magnitude is created. With an

absolute magnitude the distance can be calculated using the distance modulus,

d = 100.2(m�M+5�Ag) (5.1)

where m is the apparent magnitude in a given band, M is the absolute magnitude,


and Ag

is the extinction caused by interstellar material in the g-band. Ag

was taken

to be zero since the NGVS surveys a high Galactic latitude.

The model makes two very important assumptions. First, it only considers pure

hydrogen atmospheres (DA). Recent spectroscopic surveys have shown that DAs are

the dominant type of white dwarfs, however, one must be wary of the presence of other

types. For example, Kepler et al. (2015) reported 8441 spectroscopically confirmed

WDs in SDSS DR10 of which 6887 are DAs and 450 are helium dominated DBs. A

quick check of the Kleinman et al (2013) catalog which had previously been matched

to the NGVS reveals that 96/110 WDs have been classified as DAs. Moreover, DAs

tend to dominate in the high-temperature range which is probed in the model (>

15 000 K). Hansen & Liebert (2003) show that convection in the He layers of white

dwarfs will deposit He in the upper atmosphere. The authors suggest that this process

will increase the fraction of DB WDs below ⇠12 000 K, but that DAs will dominate

the total population of hot white dwarfs.

The second assumption is that the surface gravity (log g) is equal to 8.0, which is

typical for an average white dwarf. This was presented in Figure 2.4, which showed

that white dwarfs have typical log g values between 7.5 and 8.5. The e↵ect of these

two assumptions will be discussed in Chapter 7.

With these caveats in mind, the estimated photometric distances for the subset

of white dwarf candidates that have (g-i) < -0.5 can be seen in Figure 5.3. The (g-i)

color was used as these bands had the highest signal-to-noise of all bands. The error

bars are obtained as a result of the uncertainty in the (g-i) colour.

Figure 5.3 shows the TLUSTY model predicts that the majority of the hot white


Figure 5.3: Photometric distances for the white dwarf candidates selection in Chapter4.1. Absolute magnitudes were calculated using the TLUSTY colour-magnitude relationship in combination with the Megacam (g - i) coloursobtained as part of the NGVS. The distances were calculated using thedistance modulus with the absolute and apparent g-band magnitudes asthey have the least uncertainty. Error bars are a result of the uncertaintiesin the (g-i) colour.

5.3. DERIVING PROPER MOTIONS 56

dwarfs present in the NGVS footprint lie at distances between 200 and 2500 parsecs.

The distances calculated in Figure 5.3 suggest that the white dwarf population in the

NGVS is composed of both disk and halo white dwarfs. The distances will be used

to characterize the white dwarf population in Chapter 6.

5.3 Deriving Proper Motions

This section describes the method used to derive proper motions for a subset of the

white dwarf candidates.

5.3.1 Method

By definition, proper motion is the change in position of an object in the sky over time

attributed to its own (space) velocity. This indicates that in order to calculate the

proper motion, the position of an object must be precisely calculated from separate

images taken over a long enough baseline so that the motion can be observed. The

two catalogs used to calculate the change in position over time of the white dwarf

candidates are the SDSS and the NGVS.

The first step to calculate the proper motions for objects in the NGVS is to acquire

epochs, that is the date at which the image was taken, from both the NGVS and SDSS

datasets. The epochs for the SDSS positions were queried from the PhotoObjAll

catalog in Data Release 12 (DR 12) and are returned in modified Julian day (MJD).

The NGVS epochs were compiled by Stephen Gwyn for each NGVS pointing, also

in modified Julian day. Taking the di↵erence between the epochs yields the baseline

between each observation. The SDSS positions and uncertainties of each object are

also provided as part of the PhotoObjAll catalog, and the NGVS positions were


provided with the NGVS catalog. A typical uncertainty in position for an SDSS

object is roughly 0.02”.

Deriving the positions and epochs from the NGVS was not as straightforward as for

the SDSS. This is because the final NGVS images were created by stacking individual

images with shorter exposure times, whereas the SDSS images are composed of a

single image. As part of the acquisition process, some NGVS pointings had images

taken over a period of years. The result is that objects with high proper motion will

be smeared in the direction of motion. In order to compensate for this e↵ect, only

pointings with all images taken within 3 months of each other were used to calculate

proper motions.

5.3.2 Estimating the Uncertainty

5.3.3 Comparison to the USNO Catalog

In order to check the accuracy of the method, the resulting proper motions were

compared to the catalog published by Munn et al. (2014). Their proper motions

were derived by comparing positions using SDSS data and follow-up observations with

the Steward Observatory Bok 90-inch telescope located at the United States Naval

Observatory (USNO). The data was acquired in the r-band with an average baseline

of six years with statistical uncertainties between 5 mas/yr for brighter objects and

15 mas/yr for the faintest objects. The data is complete to r⇠22.0 and constitutes

the deepest available proper motion catalog to cover the NGVS footprint.


Figure 5.4: Comparison between proper motions in Right Ascension (RA) derived us-ing NGVS-SDSS positions and the proper motion catalog given by Munnet al. (2014). The root-mean-square deviation between the NGVS andSDSS proper motions in RA is 12.9 mas/yr.


Figure 5.5: Comparison between proper motions in Declination (DEC) derived usingNGVS-SDSS positions and the proper motion catalog given by Munn etal. (2014). The root-mean-square deviation between the NGVS and SDSSproper motions in DEC is 12.7 mas/yr.


Figure 5.6: Comparing the total proper motion measured from the NGVS-SDSSmatching technique to the proper motions derived by Munn et al. (2014).The root-mean-square deviation about the one-to-one line (red) is 9.6mas/yr.


Comparisons between the proper motions derived as part of this thesis and proper

motions from the USNO catalog can be seen in Figures 5.4 and 5.5. The red line

represents a perfect agreement between the two catalogs. The root-mean-square (rms)

deviation from the one-to-one line in RA and DEC is 12.9 mas/yr and 12.7 mas/yr

respectively. Combining the proper motions in RA and DEC results in Figure 5.6,

where the rms deviation is 9.6 mas/yr. This shows that the mean di↵erence between

the Munn et al. (2014) proper motions and those calculated using the NGVS positions

is approximately 10 mas/yr.

5.3.4 Using QSOs

In order to quantify the uncertainty associated with the method described above, a

collection of QSOs were compiled from Paris et al. (2014). QSOs were selected as

they are point-sources, but they are extragalactic in nature and therefore must have

negligible proper motions. The calculated proper motions for the collection of QSOs

can be seen in the bottom panel of Figure 5.7, where they are compared to stars and

white dwarfs. The distribution of QSOs shows minimal spread around zero proper

motion, however, the maximum observed proper motion for a QSO is ⇠10 mas/yr.

This suggests that the uncertainty associated with the NGVS proper motions is 10

mas/yr, which is in agreement with the calculated rms deviation about the one-to-one

line shown in Figure 5.6.


5.3.5 Results

Figures 5.7 and 5.8 show the resulting proper motions of the white dwarf candidates.

Many of the white dwarf candidates show very little proper motion. This is because

they are mostly part of a disk population, and hence they have Galactocentric veloci-

ties comparable to the Sun. The spread of points towards negative µRA

and negative

µDEC

is consistent with solar reflex motion, suggesting that these objects belong to

the Galactic halo. This is because stars in the halo do not rotate in the Galactic

disk with the Sun. The measured proper motion is therefore a result of the relative

motion between the Sun and the halo stars. An estimate of the tangential velocities,

as well as Galactic space velocities, will be provided in Chapter 6 as a tool to separate

potential halo white dwarfs from their counterparts in the Galactic disk.


Figure 5.7: Calculated proper motions for stars (yellow), white dwarfs (blue), andQSOs (red) using the NGVS-SDSS positions.


Figure 5.8: Proper motion in RA and DEC for white dwarf candidates in the NGVSdataset (black). QSOs are also plotted to show they display minimalchange in position between the two catalogs.

65

Chapter 6

Selecting Candidate Halo White Dwarfs

This Chapter uses a combination of models and observations in order to classify the

white dwarf candidates as belonging to either the Galactic disk or halo.

6.1 Colours and Magnitudes: Comparing to the TRILEGAL Model

TRILEGAL provides simulated stellar magnitudes for a mock catalog at a given

Galactic longitude and latitude using input stellar evolution models. This section

will detail two ways to distinguish halo white dwarfs from disk white dwarfs using

TRILEGAL: first using only g-band magnitude distributions, and second using a

colour-magnitude selection.

One feature of TRILEGAL is that is it categorizes the mock stars as being in a

thin disk, a thick disk, or the halo. The following analysis was done by extracting

the white dwarfs from each of the three Galactic components that are being probed

by the NGVS.

6.1. COLOURS AND MAGNITUDES: COMPARING TO THETRILEGAL MODEL 66

6.1.1 Using g-band Magnitudes

The first method used to determine the prevalence of disk and halo white dwarfs

is to use their relative contribution to the total population as a function of g-band

magnitude. This distribution can be seen in Figure 6.1. As expected, the relative

fraction of halo white dwarfs (green) increases towards fainter magnitudes, while the

fraction of disk white dwarfs declines. The discreteness of the plot arises as a result

of small number statistics.

In order to quantify the total number of expected disk and halo white dwarfs, the

fractions obtained in Figure 6.1 were applied to the g-band magnitude distribution

of white dwarf candidates (see Figure 5.1). The results yield 674 disk white dwarfs

and 220 halo white dwarfs, or a 75% disk and a 25% halo contribution.

6.1.2 Using a Colour-Magnitude Selection

The second method used to characterize the white dwarf population is to separate

them in a colour-magnitude diagram. A colour-magnitude diagram of the mock white

dwarfs from TRILEGAL can be seen in Figure 6.2. The black line indicates a visually

chosen line which separates the halo population (green) from the majority of the disk

population (red and yellow).

Applying the chosen colour-magnitude cut to the white dwarf candidates yields

715 disk white dwarfs and 179 halo white dwarfs, or an 80%-20% contribution from

the disk and halo respectively.

6.2. USING PHOTOMETRIC DISTANCES 67

Figure 6.1: The relative contribution to the white dwarf population by each of thethree Galactic components.


Figure 6.2: A TRILEGAL CMD for white dwarfs from each Galactic component. Theblack line was visually chosen to separate halo and disk white dwarfs.


6.2 Using Photometric Distances

Photometric distances were calculated in Chapter 5 using a colour-magnitude rela-

tionship derived using the TLUSTY model (Hanz & Lubeny, 1995) for a DA white

dwarf with a log g = 8.0. These photometric distances can be combined with the high

Galactic latitude of the NGVS field to derive model-dependent scale heights. Bovy,

Rix & Hogg, (2012) used G-dwarf stars as tracers of the thin disk and found that the

population was well fit by an exponential up to a scale height of ⇠1 kpc . Using this

value to crudely separate the disk population from the halo gives results that can be

seen in Figure 6.3.

Using the scale height of the Galactic thick disk to separate candidates results

in 74% being classified as disk white dwarfs and 26% being classified as halo white

dwarfs.

6.3 Kinematics: Using Proper Motions

The di�culty in selecting halo white dwarfs is exacerbated by the fact that they

cannot be distinguished spectroscopically from their disk counterparts (Cojocaru et

al. 2015). The Galactic halo is composed of population II stars which are more

metal-poor than disk stars. A resulting estimate of the metallicity can thus provide

a tool to distinguish between most halo and disk stars. The reason that this method

cannot be used to distinguish white dwarfs is that they lack any surface metals due

to the e�ciency di↵usion (Cojocaru et al. 2015).

6.3. KINEMATICS: USING PROPER MOTIONS 70

Figure 6.3: Calculated scale heights as a function of g-band magnitude. The dashedline indicated the chosen scale height which separates disk and halo whitedwarfs.


6.3.1 Tangential Velocities

The first method used to separate halo white dwarfs from disk white dwarfs involves

tangential velocities. The tangential velocity of an object is the velocity measured

perpendicular to the line of sight. The tangential velocity, in km/s, is related to the

proper motion,

vt

= kdµ, (6.1)

where d is the distance in parsecs, µ is the proper motion in arcseconds/year, and

k=4.74 is a constant.

The tangential velocities for the subset of white dwarf candidates which have

distances estimated from Chapter 5.2 and proper motions from Chapter 5.3 can be

seen in Figure 6.4 as a function of g-band magnitude. The error bars are a result of

the measurement errors both of the positions which are used to calculate the proper

motion and of the colours which are used to calculate the distances of the candidates.

In order to select the population of halo white dwarfs, a tangential velocity cut

of 200 km/s is imposed. This value was chosen in accordance with Cojocaru et al.

(2015) who used a population synthesis code to study the luminosity function of white

dwarfs in the Galactic halo. The value of 200 km/s was chosen as it reflects typical

heliocentric tangential velocities of a halo population. This cut is represented by the

horizontal blue line in Figure 6.4.


Figure 6.4: Calculated tangential velocities for the NGVS white dwarf candidates.The blue line indicates a tangential velocity of 200 km/s, which was thecut used to separate disk and halo white dwarfs.


Applying the tangential velocity cuts shown in Figure 6.4 results in the classifi-

cation of 13 halo white dwarfs and 118 disk white dwarfs, or a 90%-10% disk-halo

contribution.

6.3.2 Galactic Space Velocities

Coordinate Transformations

Another method that can be used to separate disk and halo stars is to use their

Galactic space velocities (U, V, W). Since these velocities are defined with respect

to the Galactic center, a transformation from equatorial (RA and DEC) to Galactic

coordinates (l, b) must be performed.

The transformation from motions in the Equatorial coordinate system to velocities

in Galactic coordinates were done in accordance with Johnson & Soderblom (1987),

but with J2000 coordinates. The Galactic space velocities are related to the motions

in equatorial coordinates using the following transformations:

2

66664

U

V

W

3

77775= B ·

2

66664

⇢

kdµ↵

kdµ�

3

77775(6.2)

where U, V, and W are the Galactic space velocities in km/s, ⇢ is the radial

velocity in km/s, k=4.74 is a constant, d is the distance in parsecs, and µ↵

and µ�

are the proper motions in RA (↵) and DEC (�) in arcsecond/year respectively. The

transformation matrix, B, is equal to


B = T ·A (6.3)

where

T =

2

66664

+cos ✓o

+sin ✓o

0

+ sin ✓o

� cos ✓o

0

0 0 +1

3

77775

2

66664

� sin �NGP

0 + cos �NGP

0 �1 0

+ cos �NGP

0 + sin �NGP

3

77775

2

66664

+cos↵NGP

+sin↵NGP

0

+ sin↵NGP

� cos↵NGP

0

0 0 +1

3

77775(6.4)

and

A ⌘

2

66664

+cos↵ cos � � sin↵ � cos↵ sin �

+sin↵ cos � +cos↵ � sin↵ sin �

+sin � 0 + cos �

3

77775(6.5)

are used to transform the coordinates. In order to perform the transformation the

location of the North Galactic Pole (NGP) in equatorial coordinates (J2000) is taken

to be:

↵NGP

= 192.859479�

�NGP

= 27.128250�

The angle between the North Celestial Pole (NCP) and the great circle passing

through the North Galactic Pole and zero degrees of Galactic latitude are taken

to be


✓o

= 122.931919�.

Plugging these values into equation 6.4 gives

T =

2

66664

�0.05488 �0.8734 �0.4838

0.4941 �0.4448 0.7470

�0.8677 �0.1981 0.4560

3

77775(6.6)

The uncertainties in the space velocities are also calculated using the method of

Johnson & Soderblom (1987):

2

66664

�2U

�2V

�2W

3

77775= C

2

66664

�2⇢

(kd)2⇥�2µ↵

+ (�dµ↵

d )2⇤

(kd)2⇥�2µ�

+ (�dµ�

d )2⇤

3

77775+ 2µ

↵

µ�

k2�2d

2

66664

b12 · b13

b22 · b23

b32 · b33

3

77775(6.7)

where � represents the uncertainty in a given variable which is represented in the

subscript. The elements of the matrix C are just the squares of the elements of B

(cij

= b2ij

).

The Johnson & Soderblom (1987) method uses the parallax angle, ⇡±�⇡

, instead

of the distance, and so the following conversions were made in the equations above:

⇡ =1

d(6.8)

where the parallax angle is in arcseconds in order to achieve a distance in parsecs.

The uncertainty is then found by propagating the error of ⇡, which yields


�⇡

=�d

d2. (6.9)

An important item to note is that the coordinate transformations require a ra-

dial velocity, ⇢, in order to be meaningful. Radial velocities are normally calculated

using the red or blue shift of a spectral line. Unfortunately, radial velocities are not

available for the white dwarf candidates selected in this work. Moreover, accurate

radial velocities are di�cult to determine due to the large surface gravities (log g

= 8.0) of the white dwarfs which contribute to a significant gravitational redshift of

the spectral lines (Cojocaru et al. 2015). For example, Falcon et al. (2010) used a

collection of white dwarfs in order to determine the mean gravitational redshift of a

white dwarf. The authors determined that the mean gravitational redshift imposed

is hvg

i = 37.50 ± 3.59 km/s. This means that any measured redshift is a combination

of the actual radial velocity as well as the contribution from the surface gravity, and

no precise method to disentangle the contributions exists (Cojocaru et al. 2015).

With an absence of radial velocity measurements, ⇢ is set to zero. This results in

an underestimation of the derived U, V, and W velocities, however the contribution

from the radial velocity to U and V is minimal at the Galactic latitude of the NGVS

field (b⇠75�). Plugging the central Equatorial coordinates of the NGVS field into

equation 6.2 yields

2

66664

U

V

W

3

77775= B ·

2

66664

⇢

kdµ↵

kdµ�

3

77775= ⇢

2

66664

0.141

�0.227

0.963

3

77775+ kdµ

↵

2

66664

�0.865

�0.327

�0.382

3

77775+ kdµ

�

2

66664

�0.523

+0.809

+0.268

3

77775(6.10)


Equation 6.10 shows that the coe�cients associated with the proper motions are

larger than those associated with the radial velocity. Plugging in typical values for

the distance and proper motions also shows that a calculated radial velocity would

be comparable to a tangential velocity (kdµ), suggesting that the U and V velocities

are dominated by the proper motion at the Galactic latitude of the NGVS footprint.

White Dwarf Candidates in the U-V Plane

With this caveat in mind, the resulting U and V Galactic space velocities of the

white dwarf candidates can be seen in Figure 6.5. The dot-dashed and dashed ellipses

represent the 2� velocity ellipsoids for the thick and thin disk populations respectively,

while the dotted black line represents the 1� velocity ellipsoid for the halo, and

the dotted red line represents the 2� velocity ellipsoid for the halo. These velocity

ellipsoids were taken from Chiba & Beers (2000).

In order to characterize the whole population, a Gaussian was fit to the U, V,

and W velocity distributions. The resulting velocity dispersions were �U

= 80 km/s,

�V

= 80 km/s, and �W

= 39 km/s. These velocity dispersions can be compared to

the Milky Way thin and thick disk population from Edvardsson et al. (1993) shown

in Table 6.1 in km/s. Since the velocity dispersions are higher than the expected

values for the thick disk this suggests that there exists a collection of halo white

dwarfs. However, since the U and V velocities are only ⇠19 km/s larger than the

expected thick disk velocities and the W velocities are equal, this suggests that the

total population is dominated by disk white dwarfs. This can also be seen in the

average V velocity, which is less than a typical thick disk population.

The velocity ellipsoids were used to estimate lower limits for the size of the thin


Table 6.1: Velocity dispersions for typical thin and thick disk populations in km/scompared to the dispersions from Figure 6.5 Table: Binney & Merrifield(1998)

hV i �U

�V

�W

Thin Disk -6 34 21 18Thick Disk -36 61 58 39Halo -180 135 105 90This Work -21 80 80 39

disk, thick disk, and halo populations by counting all of the objects which lie strictly

within each velocity ellipsoid. The total number of white dwarfs which lie strictly

within the 2 � velocity ellipsoid for the halo is 20 , or 16% of the total population

included in Figure 6.5. Using the same approach for the thick disk yields 20 such

objects, or 18% of the total population. One object lies within the overlapping region

of the halo and thick disk ellipsoids. A population of 72 objects, or 58% lie within

the 2 � ellipsoid which represents the thin disk. Finally, 10 (8%) objects lie outside

the 2� velocity ellipsoid of all three components, including four objects which have

velocities that place them outside the range of Figure 6.5, and are not classified.

Combining the selection results with the analysis of the velocity dispersions implies

that the population of white dwarfs selected in Chapter 4 is composed mainly of disk

white dwarfs, with the addition of a small population of halo white dwarfs. An

analysis of the selected halo candidates, as well as a discussion as to whether or not

they are truly halo white dwarfs is presented in Chapter 7.


Figure 6.5: Galactic space velocities for white dwarf candidates, along with the 2�velocity ellipsoids for the thin disk (dashed), thick disk (dot-dashed), andthe halo (dotted red). The 1� velocity ellipsoid for the halo is indicatedby the black dotted line.


Figure 6.6: Galactic space velocities and associated error-bars for white dwarf candi-dates.

81

Chapter 7

Discussion

This chapter discusses the results from Chapters 5 & 6 and compares the model pre-

dictions to the results obtained using kinematics. An analysis of the halo candidates

selected in Chapter 6 is presented in Chapter 7.4.

7.1 Halo White Dwarfs and Stellar Evolution Models

The combination of deep optical and ultraviolet photometry, as well as the high

Galactic latitude, makes the NGVS footprint an interesting location for identifying

halo white dwarfs. Chapter 6 began by presenting theoretical predictions from the

TRILEGAL population synthesis code for the total number of halo and disk white

dwarfs expected within the NGVS footprint. The chapter finished by using tangential

and approximate Galactic space velocities to characterize a subset of candidates as

either a disk or a halo white dwarf. The results from each method are summarized

in Figure 7.1.

Figure 7.1 shows that the fraction of halo white dwarfs predicted by TRILEGAL

7.1. DISCUSSION 82

Figure 7.1: Disk and Halo contributions calculated from each method described inChapter 6.

7.1. DISCUSSION 83

Figure 7.2: A comparison between IFMRs studied by Bianchi et al. (2011). Figure:Bianchi et al. (2011).

are ⇠10 percentage points higher than was found using the derived stellar kinemat-

ics. As previously discussed, the initial-final mass relationship (IFMR) used in the

TRILEGAL model has been shown to over-predict the total number of halo white

dwarfs. The IFMR used in the TRILEGAL model was derived by Marigo & Girardi

(2007) by combining previous pre He-flash models with an updated prescription for

the post-AGB phase. A comparison between the TRILEGAL IFMR and other IFMRs

can be seen in Figure 7.2.

Comparing the IFMRs in Figure 7.2 shows that below Minitial

=3M� the TRILE-

GAL IFMR (solid black line) predicts higher white dwarf masses for smaller progen-

itor masses. Bianchi et al. (2011) compared the mass distribution created by the

di↵erent IFMRs seen in Figure 7.2. The mass distribution which results from the

7.2. MATCHING EFFECTS 84

default IFMR is skewed towards higher masses, which does not agree with recent

spectroscopic surveys (see Figure 2.5). The high mass white dwarfs are also desig-

nated as halo white dwarfs by the model. White dwarfs with higher masses also have

higher e↵ective temperatures and bluer colours. As a result, more white dwarfs will

be created with colours within the colour cut imposed on the data (g - i < -0.3).

Bianchi et al. (2011) concluded that using the Weidemann (2000) or Kalirai (2008)

IFMR reduces the number of predicted white dwarfs in the halo and best matches the

observations. Unfortunately changing the IFMR within the model is not currently an

option, and thus a detailed comparison is beyond the scope of this work.

Another consideration in the kinematic selection used in Chapter 6 is that the

number of halo white dwarfs selected represents a lower limit. This is because of the

overlapping nature of the Galactic components in U-V space. More specifically, halo

objects can lie within the 2� thin or thick disk velocity ellipsoids seen in Figure 6.5.

For example, since the U and V velocities are heliocentric an object at (U,V) = (0,0)

could represent a star which is rotating in the disk with the Sun, or a star which is

in the halo and orbiting with a velocity roughly equal to that of the Sun.

7.2 Matching E↵ects

The methods used to construct the optical-UV and the proper motion catalog within

the NGVS footprint also had an e↵ect on the results obtained in Chapter 5 and 6.

Specifically, the number of predicted white dwarfs from TRILEGAL do not agree

with the results from Chapter 4. Possible scenarios which could result in an object

not being identified as a white dwarf are presented, and an analysis of these objects


is done to see whether or not these e↵ects could account for the discrepancy (factor

of ⇠2) between the observations and the model.

7.2.1 Photometric

The methods used to match the NGVS and GUViCS catalog also a↵ects the total

number of white dwarfs selected. This is because the lower resolution of the UV data

combined with the high spatial density of the NGVS data results in a large number

of multiple matches. Most multiple matches consist of two or more optical sources

being attributed to a single UV source. Since the UV PSF is the size of the search

radius, every optical source matched to the UV object may contribute flux to the UV

PSF. This would render meaningless colours, and such objects were discarded.

Matching the NGVS catalog to the GUViCS catalog returned 13540 instances

where multiple optical objects were attributed to a single UV object. Of the 13540

multiple matches, there are 6328 double matches (that is, instances of 2 optical ob-

jects linked to a single UV object), 268 triple matches, 17 quadruple matches, and 2

quintuple matches. Using the same g - i colour cut (g - i < -0.3) and g-band magni-

tude limit (g <24.5) as in Chapter 4 results in 151 white dwarf candidates lost due

to multiple matches.

In order to study the properties of the multiple matches, objects within the double

match category were paired together. A plot comparing the magnitudes of each

object within a pair can be seen in Figure 7.3. The plot shows the di↵erence in

magnitude between the two optical matches as a function of the g-band magnitude

of the brightest object. For example, the red circle indicates a match for which the

brightest object has a magnitude of 18.5 and a �m of approximately zero. This


Figure 7.3: The di↵erence between the brightest and dimmest objects within a doublematch scenario as a function of the magnitude of the brightest object.The red circle highlights a case where 2 objects of similar magnitude werematched to a single UV source, whereas the blue circle highlights objectsfor which the magnitude of faintest source lies near the NGVS surveylimit.

means that a single UV source was matched to two objects with roughly the same

magnitude. Conversely, the blue point indicates a pair for which the brightest object

is 18th magnitude and the faintest is more than eight magnitudes fainter. In this

case, the faintest object is close to the limiting magnitude of the NGVS survey. The

diagonal cluster of points is caused by the limiting magnitude and indicated instances

where a UV source was matched to a brighter object as well as one near the survey

limit. The ability to select the proper match in the case highlighted by the red circle


is very di�cult, and due to their proximity both objects likely contribute to the UV

PSF. However, the faintest object in the case highlighted by the blue circle likely does

not contribute to the UV PSF as it is much fainter than the brightest object.

Using the same colour and magnitude selections as in Chapter 4 returned 151

objects which were lost since they had another optical object within the search radius.

However, even with these extra 151 objects, the discrepancy between TRILEGAL and

the observations remains. Figure 5.2 showed that the model over-predicts brighter

white dwarfs by a factor of⇠2 and a factor of⇠10 at faint magnitudes, and adding 151

objects to the candidates selected in Chapter 4 does not account for this discrepancy.

An analysis of the distances between the pair of optical objects matched to a single

UV object also points to the need for higher resolution UV photometry. The average

closest separation is 1.46 arcseconds, with the next closest being 2.33 arcseconds

away. This shows that UV photometry with a PSF comparable to the NGVS would

be able to resolve both of the optical sources. Future projects are targeting this type

of resolution in the UV, and these will be discussed in Chapter 8.

7.2.2 Astrometric

The NGVS proper motions were calculated by matching SDSS and NGVS data within

a 1 arcsecond radius. Since the average baseline between the observations was 7.25

years, the maximum observed proper motion would be 138 mas/yr. This means that

an object with a proper motion higher than this would not be included in the matched

catalog as the change in position would be larger than the search radius. This is most

likely to a↵ect very close objects, as they typically have larger proper motions. Since

this thesis focused on objects which are located at distances of more than 1 kpc

7.3. SOURCES OF BIAS 88

and magnitudes fainter than g=20.0, the proper motion limit should not a↵ect the

selection of halo objects.

7.3 Sources of Bias

Throughout this work, selection tools and model parameters were chosen based on

previous spectroscopic surveys or models. The e↵ect that these choices had on the

results presented in Chapters 5 & 6 are explored in this section.

7.3.1 Colour Cuts

Chapter 4 presented a selection tool using the locations of spectroscopically confirmed

objects in a colour-colour diagram. The goal of the chosen colour cuts was to select as

many spectroscopically confirmed white dwarfs while minimizing the contamination

from QSOs and other blue objects such as blue horizontal branch stars, O and B

sub-dwarfs, and hot main sequence stars. The colour cuts can be seen in Figure 4.2.

As Figure 4.2 (p.42) shows, many spectroscopically confirmed white dwarfs are

not included in the candidate selection as their colours are indistinguishable from

QSOs and main-sequence stars. Using the Kleinman et al. (2013) catalog from SDSS

DR7 shows that the (g - i) < -0.3 used in this work corresponds to a temperature cut

of ⇠10 500 K. This can be seen in Figure 7.4, where the dashed vertical line indicates

the colour cut used in Chapter 4 and the dotted horizontal line represents 10 500 K.

The resulting temperature cut means that the candidates selected in Chapter 4

are hotter than ⇠10 500 K, and thus are younger than the vast majority of halo white

dwarfs (ex. Bergeron 2005). As a result, many of the ultra-cool white dwarfs were


Figure 7.4: The e↵ect the colour selection from Chapter 4 has on the temperature ofthe selected white dwarfs.

not selected even though they represent the oldest white dwarfs in the Milky Way.

This makes them prime candidates to be remnants of the halo population. These

objects continue to remain elusive at large distances as they can not be separated in

colour-colour space. A di↵erent approach, such as using proper motion data from the

upcoming Gaia mission, must be taken in order to cleanly identify these ultra-cool

white dwarfs. This colour cut will, however, select recently formed halo white dwarfs

which are comparable to the objects described by Kalirai (2012). These objects

represent the youngest white dwarfs in the Galactic halo and have temperatures

between 14 000 K and 20 000 K.


7.3.2 Model Parameters and their E↵ect on Distance Estimates

Chapter 5 made use of a colour-magnitude relationship to estimate distances to the

white dwarf candidates. The relationship assumed that all of the candidates had

hydrogen atmospheres (type DA) and a log g=8.0. These assumptions were made

based on spectroscopic surveys from the SDSS, which showed that ⇠80% of white

dwarfs are DAs and the distribution of surface gravities peaks at log g⇠8.0 with

minimal scatter (as seen in Figure 2.4, p.14).

Holberg & Bergeron (2006) calculated cooling models for both DA and DB white

dwarfs in the SDSS band-passes1 for a range of temperatures and surface gravities.

These cooling models can be used to estimate the e↵ect of type and surface gravity

on estimated distances. First, a plot of the colour-absolute magnitude in the SDSS g

and i bands can be seen in Figure 7.5. The figure shows that the DA and DB colour-

absolute magnitude relations agree rather well in the blue regime, and only begin

to deviate at colours which are redder than the colour selections used in Chapter 4.

Hence, the choice of type does not have an impact on the estimated distances in this

thesis.

One important feature of Figure 7.5 is the turn-over of absolute magnitudes at

(SDSS g - SDSS i) ⇠1.75. The turn-over is a result of collision-induced opacity in the

red portion of the white dwarf spectrum (Hambly 2001; Hu et al. 2013). This occurs

below temperatures of ⇠3500 K, and results in these ultra-cool white dwarfs having

similar (g - i) colours to their hot counterparts. However, this has minimal e↵ect in

the ultraviolet and thus the resulting (NUV - g) colours for these ultra-cool white

1http://www.astro.umontreal.ca/ bergeron/CoolingModels


Figure 7.5: A comparison between the colour-absolute magnitude relationships of aDA and DB white dwarf in SDSS band-passes from Holberg & Bergeron(2006).

dwarfs are still very red. This results in the ultra-cool white dwarfs falling outside

the colour selections made in Chapter 4.

Holberg & Bergeron (2006) also generated cooling models with di↵erent surface

gravities. Figure 2.4 showed that the majority of white dwarfs have log g ⇠ 8.0,

however, there is a small range of surface gravities above and below the peak. Figure

7.6 (p.14) shows the colour-absolute magnitude relation for DA white dwarfs with

log g = 7.5, 8.0, and 8.5. The figure shows that the chosen value of log g has an

e↵ect on the estimated distance. The average di↵erence in magnitude between 0.5


Figure 7.6: Colour-absolute magnitude relationships for DA white dwarfs with di↵er-ent values of log g.

dex of log g is 0.49 g-band absolute magnitude. Plugging the di↵erence in absolute

magnitudes into equation 5.1 reveals that the distances to objects with log g=7.5

are overestimated by 25% and objects with log g=8.5 are underestimated by 20%.

These large uncertainties are minimized since the majority of white dwarfs have log

g between 7.8 and 8.1, suggesting an uncertainty closer to 10-15% on the distance

estimates.

7.4. ANALYSIS OF HALO CANDIDATES 93

7.3.3 Radial Velocities

Since the majority of the white dwarf candidates selected in this thesis do not have

available spectroscopic measurements, the radial velocity was set to zero when the U

and V Galactic space velocities were calculated. Moreover, even with spectroscopic

measurements the shift in a given spectral line has an unknown contribution from the

strong surface gravity. Falcon et al. (2010) calculated the mean radial velocity that

is induced by the surface gravity as 32.57±1.17 km/s. This result shows that even

white dwarfs with zero line-of-sight motion relative to the Sun will display a shift in

their spectral lines.

Despite the uncertainty introduced from the gravitational redshift, setting vr

=0

should not significantly a↵ect the result obtained in this thesis. This is because the

U and V velocities are dominated by the tangential motion at the Galactic latitude

of the NGVS survey as shown in equation 6.10.

7.4 Analysis of Halo Candidates

Chapter 6 presented two selection tools based on kinematics: tangential velocity and

Galactic space velocities. Using the halo velocity ellipsoids in Figure 2.6, twenty

halo candidates were identified. This section analyzes known properties of these

candidates, and discuses whether or not they could be part of the Galactic stellar

halo. Table 7.1 displays the candidates with Galactic space velocities that places

them solely within the 2� halo velocity ellipsoid.

Using the distance and tangential velocity selections from Chapter 6 suggests that

candidates 2, 5, and 13 are the most promising halo candidates, as they have distance


Table 7.1: Properties of halo white dwarf candidates selected based on their Galacticspace velocities. All velocities are in km/s

ID RA DEC u g i d (pc) vt

U V1 186.716 11.35719 21.11 21.43 22.17 1382 151.1 131.9 -78.462 187.1925 6.175394 21.91 21.92 22.6 1498 280.2 -79.08 -230.13 187.3195 5.420099 22.99 22.62 23.13 1503 129.4 -10.19 -111.54 187.4864 12.93748 21.7 21.43 22.02 1010 102.6 -66.68 76.825 186.3981 10.15334 22.44 22.32 22.97 1717 205.2 -124.6 -138.46 186.4843 7.921526 21.76 21.72 22.35 1251 140.6 98.58 83.357 186.598 7.019057 19.23 19.29 19.89 379.4 115.6 48.66 -95.448 185.709 7.053767 23.01 22.68 23.22 1629 139.9 -133.4 -32.819 185.2923 14.77345 23.05 23.13 23.78 2472 182.2 179.1 24.9910 184.5033 7.133588 23.29 22.95 23.48 1814 151.1 -123.3 -73.8711 183.631 10.81119 21.75 21.73 22.24 1006 113.8 21.73 -103.512 187.8255 13.86469 21.44 21.27 21.81 841.7 120.3 53.48 -102.713 189.1173 8.405205 22.46 22.39 23.12 2098 439.2 -34.56 -379.214 189.3158 8.05422 22.56 22.55 23.21 1906 137.1 -138.1 14.5415 189.1216 11.96684 22.83 22.57 23.13 1614 124.1 -95.67 81.1616 189.4331 13.55116 22.7 22.23 22.73 1248 170.2 25.8 -152.717 190.1759 13.15993 20.23 20.56 21.23 784.6 155.8 -138.1 -50.2418 191.1537 11.39216 22.33 22.21 22.83 1538 112 98.22 -60.5819 190.5332 11.54052 22.29 22.04 22.61 1272 118 -94.87 73.7420 191.6986 12.80479 21.89 22.04 22.75 1721 146.1 142.6 -52.57

estimates over 1 kpc and tangential velocities greater than 200 km/s. Their (g - i)

colours suggest that they have temperatures between 18 000 - 23 000 K, meaning

that they are fairly young, and are comparable to the candidates studied by Kalirai

(2012). This suggests that they could be some of the newest white dwarfs belonging

to the inner halo. Follow-up spectroscopy will be needed to determine the type,

temperature, and log g of each candidate in order to confirm whether the candidates

are true halo white dwarfs.

One noteworthy object is candidate 7, as it has an SDSS spectrum. The spectrum


Figure 7.7: The SDSS spectrum of halo white dwarf candidate 7 with common spec-tral lines indicated. The strong, broad, Balmer series is present indicatingthat it is a DA. Figure: SDSS DR12 (Alam et al. 2015)


can be seen in Figure 7.7 and features the strong, broad, Balmer lines which are a

staple of DA white dwarfs. It is hot (Teff

⇠18 500 K) and bright (g = 19.29)leading

to a distance estimate of 379±25 pc. This object is likely part of the Galactic thick

disk, as its tangential velocity is only 115 km/s and its location in U-V velocity space

places it just outside the 2� thick disk ellipsoid.

Oppenheimer et al. (2001) also found many objects comparable to candidate 7.

These objects were identified as young, hot, white dwarfs with halo-like velocities.

Bergeron (2003) studied these candidates and proposed a scenario to explain their

existence. The proposed scenario dictates that they are remnant donor stars in a

binary system in which the companion exploded as a supernova. The resulting donor

star would be ejected into the halo with a large velocity and continue to evolve until it

became a white dwarf. The resulting white dwarf would thus have halo-like velocities.

The candidates that come from Oppenheimer et al. (2001) for which Bergeron

(2003) proposed this mechanism were much brighter than the candidates selected in

this thesis. This suggests that the Oppenheimer et al. (2001) objects are likely closer

than the candidates in Table 7.1. For this reason, these objects are likely to be the

most recently formed halo white dwarfs, similar to the ones studied by Kalirai (2012),

however precise determinations of their type and distances must be achieved before

any definitive conclusions can be drawn.

97

Chapter 8

Summary and Conclusions

This thesis made use of optical and UV photometry to characterize the white dwarf

population within the footprint of the Next Generation Virgo Custer Survey with a

particular emphasis on recently formed white dwarfs within the Galactic halo. Colour-

colour diagrams were used to separate the spectroscopically confirmed white dwarfs

from main-sequence stars, QSOs, and blue horizontal-branch stars/blue stragglers. A

colour cut in both (NUV - g) and (g - i) was used to select white dwarf candidates.

This colour selection tool was compared to model tracks provided by Luciana Bianchi,

and these tracks agreed with observations.

Once the white dwarf candidates were compiled, their various photometric and

kinematic properties were presented. Their magnitude distributions in the g, i, and

NUV bands highlighted the depth of the combined NGVS-GUViCS catalog, as can-

didates were selected with higher frequency at fainter magnitudes. The g-band mag-

nitude distribution was compared to the TRILEGAL population synthesis code and

showed that the model over-predicted the observed number of white dwarfs. The

over-prediction can be explained by a combination of observational constraints and

98

model inputs. The data su↵ers from incompleteness below g⇠22 as a result of the

limiting magnitude of the NUV data. Furthermore, the choice of IFMR tends to

overproduce white dwarfs within the model, specifically halo white dwarfs at faint

magnitudes.

Photometric distances for the candidates were calculated using the TLUSTY

colour-absolute magnitude relationship. The model assumed that the white dwarfs

were DAs with log g = 8.0. The e↵ect that these choices had on the calculated dis-

tances was discussed in Chapter 7. This discussion showed that the choice of type

had little e↵ect, however, the choice of log g introduced an uncertainty of up to 25%.

The calculated distances showed that the candidates lie at distances between 200 and

3000 pc.

Proper motions were derived for a subset of candidates by comparing their NGVS

and SDSS positions. The average baseline values in the observations was 7 years.

The calculated proper motions were compared to the USNO catalog and showed an

agreement for motions greater than 10 mas/yr. A set of QSOs was studied to make

sure that these objects showed minimal proper motion, and the maximum calculated

proper motion for a QSO was 10 mas/yr. This suggests that the uncertainty associ-

ated with the proper motions calculated using the NGVS positions is 10 mas/yr.

The candidates were then classified as either disk or halo white dwarfs based

on a variety of properties. First, the TRILEGAL model made predictions for the

relative contribution from the disk and halo to be roughly 75%-25% respectively.

This was done using the theoretical g-band magnitude distribution, as well as a

colour-magnitude selection.

99

With the theoretical predictions in mind, the photometric distances and kinemat-

ics were used to separate disk and halo white dwarfs. The photometric distances

were converted to a scale height using the Galactic coordinates of the white dwarf

candidates. A scale height of 1 kpc was chosen to separate the disk from the halo.

The resulting selection returned a 76%-24% disk-halo contribution, which agrees with

the TRILEGAL model.

Tangential velocities were calculated using the proper motions and photometric

distances of the candidates. Using a tangential velocity of greater than 200 km/s to

select halo candidates returned 13 objects. This represented a 90%-10% disk-halo

contribution.

Galactic space velocities were calculated using the coordinate transformations

from Johnson & Soderblom (1987), the photometric distances, and the proper motions

in RA and DEC. Since radial velocities were not available, ⇢ was set to zero. This

assumption did not have a large e↵ect on the U and V velocities because of the high

Galactic latitude of the NGVS footprint. The velocity dispersions in U, V, andW were

calculated and compared to typical thin disk, thick disk, and halo populations from

Binney & Merrifield (1998). The dispersions showed that the white dwarf candidates

are likely dominated by a thick disk population, but should contain halo candidates.

Velocity ellipsoids for the thin disk, the thick disk, and the halo were plotted in

U-V velocity space, and objects which lied strictly within the 2� ellipsoid for the halo

were classified as halo white dwarfs. This method returned twenty halo candidates

and a 76%-16% disk-halo contribution. Objects which did not lie within any of the

velocity ellipsoids were not classified, accounting for the remaining 8%.

8.1. FUTURE WORK 100

This thesis concluded by analyzing the twenty halo candidates selected using their

Galactic space velocities. Their (g - i) colours suggest that they have temperatures

between 18 000 K and 23 000 K, which are comparable to the candidates described by

Kalirai (2012). These objects likely belong to the inner halo and represent recently

formed white dwarfs from low-mass progenitors.

8.1 Future Work

In order to definitively conclude whether these objects are truly halo white dwarfs

they must be studied spectroscopically. Obtaining spectra for the candidates would

determine their spectral type, as well as allow their temperature, surface gravity,

and mass to be calculated. These parameters can be combined with stellar evolution

models to estimate the age of the population, as was done by Kalirai (2012).

Obtaining spectra for these objects will be di�cult because they are faint (g⇠22.0)

and have a low spatial density. Using the Gemini GMOS-N integration time calculator

shows that thirty minutes of dark time would be needed to get an average signal-to-

noise per spectral pixel of ⇠5, which is enough to determine the type (Kleinman et al.

2013). However, a signal to noise per spectral pixel of ⇠50 is needed in order to calcu-

late an accurate temperature, surface gravity (log g), and mass (Gianninas, Bergeron

& Dufour 2011, Gianninas et al. 2011). An integration time of approximately two

and a half hours would be needed in order to achieve such a signal-to-noise.

The Mauna Kea Spectroscopic explorer (MSE) will be able to identify and study

halo white dwarfs. MSE is a proposed replacement for the Canada-France-Hawaii

telescope and is planned to have a dedicated fiber-fed spectrograph on an 11m tele-

scope (McConnachie et al. 2016). MSE will also have a 1.5 deg2 field of view, and


will allow for the simultaneous acquisition of over 2500 spectra. The field of view,

combined with the collecting area, makes this proposed telescope an ideal instrument

to further study these candidates.

While MSE is not expected to be commissioned until the mid-2020s, the Gaia

mission will provide important astrometric data for these candidates within the next

few years. Gaia will provide photometry, parallaxes, and proper motion data over

the whole sky to a limiting magnitude of V ⇠ 21 (Torres et al. 2005). Carrasco et al.

(2014) theorizes that Gaia will discover between 250 000 and 500 000 white dwarfs,

and that some will have temperatures <5000 K. Parallaxes with uncertainties below

10% will be available for 95% of these objects, which will allow for precise distance

determinations which are independent of any models. The distance and kinematic

information will allow for a quick selection of halo candidates, and these objects can

be followed-up spectroscopically.

Higher resolution UV imaging will soon be available from the Ultraviolet Imaging

Telescope (UVIT). UVIT is being flown as part of the Indian Astrosat mission and

will provide ⇠1.5 arcsecond resolution imaging in the FUV and NUV bands (Kumar

et al. 2012). Even further progress can be made by the proposed Cosmological

Advanced Survey Telescope for UV and Optical Research (CASTOR), which aims

to provide HST quality imaging (⇠0.15” resolution) in the UV (Cote 2012). These

projects will be able to probe the hot white dwarf population to greater distances,

while calculating properties such as their ages, masses, and spatial distribution (Cote

2014). These missions will also be able to resolve many of the UV sources which were

matched to multiple optical objects.


The CFHT Legacy for the U-band All-sky Universe (LUAU) survey will also be

able to identify halo white dwarfs (McConnachie et al. 2016). LUAU will survey a

large fraction of the northern hemisphere with the new Megacam u-band to depths

of u⇠24.5. LUAU will provide u-band photometry which is approximately three

magnitudes deeper than SDSS, and will also provide more accurate astrometry. LUAU

was proposed to increase the number of known white dwarfs by an order of magnitude,

and will include a population of halo white dwarfs. This survey can be combined

with SDSS optical photometry, as well as the kinematic data from Gaia, and will be

available in 2017.

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