Hakeem M. Oluseyi1

A. Becker2, J. Bloom3, Z. Ivezic2, P. E. Nugent3,4, J. Richards3, K. Stassun5

N. DeLee5, M. Paegert5, B. Sesar6, D. Starr3

D. Chesny1, P. Regencia1

C. Culliton1, M. Furqan1, Keri Hoadley1, Maulik Patel1, Akeem Wells1

1Department of Physics and Space Sciences, Florida Institute of Technology2Department. of Astronomy, University of Washington

3Department. of Astronomy, University of California, Berkeley4Computational Cosmology Center, Lawrence Berkeley National Laboratory

5Department. of Astronomy, Vanderbilt University 6Department. of Astronomy, California Institute of Technology

Map 107 108 Milky Way stars

in a 9-dimensional space* that samples

diverse Galactic environments

Goal:

*3position, 3velocity, metallicity, age, type/subtype

Motivation: Near-field Cosmology

Motivation: Near-field Cosmology

-CDM cosmology predicts:Hierarchal galactic assembly

Simulations predict:Substructure in Halos of Milky Way sized galaxies

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Inner halo more metal-rich~9 Gyr

Outer halo more metal-poor< 5Gyr

Simulations predict:A two-component dual halo

We may also measure the Galactic potential

Autochthonous Cepheid PL Relations

Sandage & Tamman (2006)

Identification of lots of variable stars in diverse environments may allow local high-precision characterization of the Cepheid P-L relation across a broad range of metallicities, ages, and environments testing the P-L relations linearity and calibrating its slope and zero-point.

Figure shows a comparison of the period-luminosity relation between the LMC and Galactic Cepheids.

1. Constrain N-body simulations of galaxy formation, evolution and environments

2. Constrain -CDM models of universal evolution

3. Map the Galactic potential

4. Measure the variability baseline

5. Measure the demographics of variability

6. Calibration of the stellar distance ladder

7. Rare species

8. Stellar physics

Science Outcomes

8.4m primary mirror

9.6 deg2 FOV

215s exposures

30 TB per night

10 year survey

Data are public

First light 2018

The Large Synoptic Survey Telescope (LSST)

Map 107 108 Milky Way stars

in a 9-dimensional space* that samples

diverse Galactic environments

Goal:

*3position, 3velocity, metallicity, age, type

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SDSS Stripe 82 RR Lyrae Stars

RRab30 stars

RRc10 stars

SDSS Stripe 82 RR Lyrae Stars

SDSS Stripe 82 RR Lyrae Stars

Simulated LSST Survey

40 RRLs 1007 fields 15 mags 6 filters 10 surveys = 36,252,000 LCs

UC

MW

DD

SS

OL

Oluseyi et al. (2012)

Work Flow

Is it variable?Is the variability periodic?

What are the periods?What is the detailed shape of the folded light curve?

Analyses (Type/Subtype, FAMs, [Fe/H])Visualization

Science!

UC10 years of data1 year of data

Simulated LSST Survey

DD

Simulated LSST Survey

10 years of data1 year of data

Defining Period Recovery

N P

max

How much error can you stand?

|Pin

Pout

|Pin

maxPint

Details of survey and phenomenon:

|Pin

Pout

|P 2in

105day1

LSST RRLs:

Oluseyi et al. (2012)

Period recovery efficiency

wi =Pbsi

=nbsiN

,

NiX

i=1

wi = 1

!

Recovery Eciency =

NfPk=1

NsPi=1

wkiij

Nf

Weight stars in sample that uniformly samples period space in order to mimic nature-provided period distribution:

Light curve shape recovery

mi (t) = hmi+10X

k=1

sin [2kft+ k]

nm = nm mn

[Fe/H] = 1.345(s)31 5.394P 5.038

31 = |in31 out31 |

Two methods: [1] direct Fourier decomposition[2] fit template; Fourier decompose template

(Jurcsik & Kovcs, 1996)

Light curve shape recovery

Evolution of 31 distributions

Light curve shape recovery efficiency

Template fitted shape recovery superior in all cases

ISSUES

Previous analyses preceded in the standard single-color approach: the same period was required to be recovered in two of the gri passbands, analyzed independently:

How do we use multiple passbands of data simultaneously to recover periods more efficiently:

Sesar et al. (2009) defined ugriz single-band templates light curves. For RRab stars: 20 templates per band; for RRc stars: 2 templates per band.

Can we define template - surfaces for SDSS / LSST filters?

Can we design higher precision metallicity relations using surfaces?

m () .

m (,)?

Supersmoother2D Period recoveryw/ Joseph Richards and Josh Bloom

The correct period of a variable star will have a smooth variation in phasewavelength space.

Supersmoother2D:

Choose the period with best fit of a smoothly-varying thin-plate spline across phasewavelength space.

...see Joseph Richards talk on Friday!

Supersmoother 2D Period recoveryw/ Joseph Richards

...see Joseph Richards talk on Friday!

Supersmoother2D

outperforms single-band period recovery in initial tests using simulated LSST light curves of RRLs.

Hierarchical Clustering for 2D RRL Templates

g r

Dendrograms are calculated from difference matrices thatreduce each single-band difference light curve

to a single rms value.

Application to single bands was applied first to verify method against Sesar et al.s (2009) results.

Hierarchical Clustering for 2D Templates

Dendrograms are calculated from difference matrices thatreduce each five-band - difference surface to a single rms value.

RRab RRc

Hierarchical Clustering for RRab Stars

Cluster 1 Cluster 2 Cluster 3

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Phase PhasePhase

Phase PhasePhase

Hierarchical Clustering for RRc Stars

Cluster 1 Cluster 2 Cluster 3

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elen

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()

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Phase PhasePhase

Phase PhasePhase

Data Visualization:VIDA Astroinformatics Portal

Growing Set of Web Based Tools Filtergraph

Upload and plot your data Scatter Plots, Histograms, and data tables Large data sets Share your plots with collaborators

LC Animator Visualize Light Curve Formation Publicize your data

http://www.vanderbilt.edu/astro/vida

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Future Tools

Growing Set of Web Based Tools Variable star classifier Period Finders

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Now it is your turn!

http://www.vanderbilt.edu/astro/vida

To be continued...