The 2dF galaxyredshift survey

John Peacock & the 2dFGRS team

Harvard, October 1999

Outline

Brief history

2dF Survey motivation & design

Survey data

Spectral classification

Results:

– Luminosity function

– Correlation function

Galaxy bias & future issues

History of galaxy clustering 1930s: Hubble lognormal cell counts 1940s/50s: Eyeball surveys (Shane & Wirtanen,

Zwicky, Abell…) 1970s: Correlation functions

mass= 0(1+) A(r) = < (x) (x+r) >

(1) Autocorrelation function

(2) Two-point correlations

r prob(pair) = <n>2 dV1 dV2 [1 + 2(r) ]dV1

dV2

A(r) = 2(r) ? - only if Poisson Clustering Hypothesis is true

Meaning of clustering

Neyman Scott & Shane (1953): random clump model

r (r < R) ”r

obs: r”= 2.4?

Modern view: gravitational instability of (C)DM

- sheets, pancakes, filaments, voids...

Las Campanas Redshift Survey~25000 z’s

CfA/SSRS z-survey

~15000 z’s

Redshift Surveys

The 2dF Galaxy Redshift Survey

Aim for LCRS X 10 = 250,000 z’s Increase sky coverage to get fully 3D sample

– Measure >100-Mpc power– Test Gaussian nature of linear fluctuations – Measure redshift-space distortions

Increase sampling density– Spatial distribution for different galaxy types– Tests of theories for biased galaxy formation

2dFGRS Survey Team Australian team members:

Matthew Colless, Joss Bland-Hawthorn, Russell Cannon, Warrick Couch, Kathryn Deeley, Roberto De Propris, Karl Glazebrook, Carole Jackson, Ian Lewis, Bruce Peterson, Ian Price, Keith Taylor.

British team members: Steve Maddox, John Peacock, Shaun Cole, Chris Collins, Nicholas Cross, Gavin Dalton, Simon Driver, George Efstathiou, Richard Ellis, Carlos Frenk, Ofer Lahav, Stuart Lumsden, Stephen Moody, Peder Norberg, Shai Ronen, Mark Seabourne, Robert Smith, Will Sutherland, Helen Tadros.

2dFGRS parameters Galaxies: bJ 19.45 from revised APM

Total area on sky ~ 2000 º 250,000 galaxies in total, 93% sampling rate Mean redshift <z> ~ 0.1, almost all with z < 0.3

2dFGRS geometry

NGP

SGP

NGP 75x7.5 SGP 75x15 Random 100x2Ø ~70,000 ~140,000 ~40,000

~2000 sq.deg.250,000 galaxies

Strips+random fields ~ 1x108 h-3 Mpc3

Volume in strips ~ 3x107 h-3 Mpc3

Tiling strategy‘2dF’ = ‘two-degree field’ = 400 spectra

Efficient sky coverage, but variable completeness

High completeness through adaptive tiling: multiple coverage of high-density regions

Sampling: 2dF vs LCRS

2dFGRS (~93%)

LCRS (~25%)

Calibrating photometry

Recalibrated number counts

Old APMcounts

RecalibratedAPM counts

The 2dF site

Prime Focus

The

2dF

faci

lity

2dF on the AAT

Configuring fibres

>12 arcsec spacing; 15 degree bend

<10 seconds to position each fibre

Data pipeline: real-time X-corr z’s

Exam

ple

spec

tra

Survey status - August 1999

Observed:– 227/1093 fields– 58764 targets– 4037 repeats

Redshifts/IDs:– 53192 (91% complete)– 50180 galaxies– 2993 stars, 19 QSOs

Redshift yield

The median redshift yield is 93%.

10% of fields have a yield less than 80%.

30% of fields have a yield less than 90%.

After ADC s/w fix, good conditions routinely give yields >95%.

Reliability: of 1404 z’s in overlap with LCRS, only 8 disagree (99.4% agree).

Completeness Redshift completeness is

>90% for bJ<19 but drops to 80-85% at bJ=19.45.

Completeness is similar in NGP and SGP strips.

Completeness as a function of magnitude varies with the overall completeness of the field.

Selection function depends on (at least) overall completeness and magnitude.

Survey mask

NGP

SGP

Cutouts are bright stars and satellite

trails.

Selection maskNGP

SGP

‘Bitten-cookie’ effect from missing overlap tiles.

0% 100%

0% 100%

Stellar contamination

Contamination by objects with

z~0.

SGP

NGP

Typical level of stellar

contamination is <5%.

0% 20%

0% 20%

Survey rate At least some data were

obtained on 61/99 of nights so far allocated to survey.

Over all nights with any data, the mean number of fields/night is 4.5.

Averaged over year, expect to get 7 fields for each completely clear night = 3000 z’s per night

Full survey requires about 100 clear dark nights, or all dark time for 1 year. In practice 2dFGRS uses about 1/3 AAT dark and will take 3 years

Cone diagram: all declinations

Cone diagram: 4-degree wedge

The big picture

2dFGRS

The 2dF galaxy + QSO redshift surveys

50180 galaxies

6824 QSOs

Redshift distributionMean redshift <z>=0.11;

almost all z<0.3.

N(z) still shows significant clustering.

(mean K-corrections)2 fit to

1/Vmax LF

Overall luminosity function

STY Schechter fit gives -1.2 (due to clustering? non-Schechter form?)

Small numbers at MB>-14.

Mean spectrum

PC1

PC2

PC3Early

Late

Early

Late

Spectral classification by PCA Apply Principal Component

analysis to spectra. PC1: emission lines correlate

with blue continuum. PC2: strength of emission

lines without continuum. PC3: strength of Balmer lines

w.r.t. other emission. Classify spectral types in

PC1-PC2 plane using sample of Kennicutt to set bounds.

Further work:– effect of spectro-photometric

errors;– self-classification algorithms;– calibration against spectral

models.

STY fit1/Vmax LF

Early

Late

M*

All

LFs by spectral type

For 12,000 galaxies with PCA types, fit LFs by type.

1/Vmax LFs have less-steep faint ends than STY fits of Schechter functions.

From early to late types… – M* gets fainter: -

19.6 -18.9 gets steeper:

-0.7 -1.7 Overall M* brighter than M*

of any type; Schechter function not adequate fit.

Evidence for upturn at faint end of LF.

Overall STY Schechter fit

Sum of Schechter fits to each type

Early types (1,2)Late types (3,4,5)

Galaxy distribution by type

2D correlations r

Model comparison - IFlattening depends on 0.6/b (gal = b mass)

infall

Fingers of God

Model comparison - IIAnalyze into Legendre polynomials:

get from quadrupole-to-monopole ratio

-P2/P0 =

Linear

Damping

by fingers

of God

Projected correlations (r)

dxxrr )]([)( 2/122

APM w()

deprojection

works well to

r = 20 h-1 Mpc

(cosmic variance matters on larger scales)

The CDM clustering problemNon-monotonic scale-dependent bias

CDM CDM

Jenkins et al. 1998 ApJ 499, 20

b2 = g / m

Numerical galaxy

formation

Durham

Munich

Santa Cruz

Edinburgh

...

Antibias in LCDM

Benson et al.

astro-ph/9903343

Dark-matter haloes and bias

Moore et al:

= [ y3/2(1+y3/2) ]-1; y = r/rc

Correlations from smooth haloes

kdk

krkrk sin)(2

PS++ mass function and NFW++ halo profile gives correct clustering

CDM

CDMLin

NLAPM

Halo occupations depend on massPS++ mass function wrong shape for cluster/group LF

Correct weighting of low-mass haloes predicts antibias

CDM

Summary 2dF survey status

– Over 50,000 redshifts (20% of survey)– Expect 100,000 March 2000– 250,000 March 2001

Preliminary results – Luminosity functions by spectral type– Correlations and redshift-space distortions

Future issues– Clustering on 100-Mpc scales– Gaussian nature of density field– Clustering by spectral type and luminosity– Detailed tests of halo-based bias models