The star-formation history of mass-selected galaxies in the VIDEO survey

Transcription

1 The star-formation history of mass-selected galaxies in the VIDEO survey Jonathan Zwart 18 September, 2013

2 OVERVIEW Measuring Star-Formation Rates (SFRs) The VISTA Deep Extragalactic Observations (VIDEO) Survey (Jarvis+ 2013) SFRs from radio stacking Results Some thoughts on SKA + Euclid Thanks: Matt Jarvis, Roger Deane, Dave Bonfield, Kenda Knowles, Nikhita Madhanpall, Hadi Rahmani, Dan Smith (submitted to MNRAS) 1

3 (Hopkins and Beacom 2006) 2

4 MEASURING STAR-FORMATION RATES Fundamental: Want SFR Density as a function of redshift ( Madau plot ) need SFRs UV/optical: reach high z, but suffer from dust obscuration Submm FIR: dust re-emission; limited by poor telescope resolution and need (from ground) for good weather X-rays: from binaries/snrs Radio: cosmic rays and synchrotron from SNRs, surprisingly tight relation to SFR, calibrated to radio FIR correlation and circumvents the dust extinction problem; high resolution, need deep-ish radio data ALL susceptible to AGN contamination 3

5 VIDEO SURVEY: AIMS One of new wave of deep near infra-red surveys Trace galaxy evolution over 0 < z < 4 How and when did massive galaxies form? When did they assemble most of their stellar mass? Where does mass assembly happen? Plus clusters, accretion activity, supernovae... Advantage: K-band selection selection by M z (photospheric-emission peak) 4

6 VIDEO SURVEY: TECHNICAL XMM3 field (here) 1.5 sq. deg November 2011 November Photometry: VIDEO (ZY JHK s ) + CFHT (ugriz) z phot /(1 + z) = 0.13, z [0, 5] (OK Bauer+ 2011) 5-σ K s = 23.5 in 2 -diameter aperture Now divide data into subsamples: 5

7 CLASSIFICATION We opt for classification by best-fit templates (from LE PHARE i.e. 62 interpolated Coleman+ (1980) templates; fit z then M ): Ellipticals Sbc, Scd, irregulars, etc. Starburst galaxies 4.4. PHOTOMETRIC REDSHIFTS 81 Figure 4.2: Empirical galaxy template SED library used by Le Phare in estimating photometric redshifts. The original optimised templates from Arnouts et al. (2007) are plotted as 6 solid black lines and all extrapolated templates are plotted as grey dotted and dashed lines.

8 DATA SELECTION (INFRA-RED) z < 4.0 K < 23.5 (selection in K selection by stellar mass) Various technical cuts... Excise stars via colour cut (Baldry+ 2010) 59,000 galaxies remain (full sample) 7

9 REDSHIFT DISTRIBUTION 8

10 STACKING Key idea: Stack radio pixels at positions of K-band sources Common technique for spectra etc. increase S/N at expense of losing information on individual objects detect sources below flux limit ( 20µJy in this case 0.2µJy) Allows us to comment statistically on the different galaxy samples Use the median (at every subsequent step) to avoid sensitivity to outliers 9

11 1.4-GHZ VIMOS VLA DEEP SURVEY (BONDI+ 2003) 1000 sources at > 5σ = 100µJy; 6 beam (McAlpine talk) 10

12 POSTAGE STAMPS: MEAN (UPPER) V. MEDIAN (LOWER) Dec/arcsec RA/arcsec µjy/b Dec/arcsec RA/arcsec µjy/b Dec/arcsec Dec/arcsec RA/arcsec µjy/b RA/arcsec µjy/b 11

13 DUE DILIGENCE Noise integrates down as n Realisations at random positions are consistent with noise Two independent pipelines agree 12

14 FROM FLUXES TO LUMINOSITIES TO STAR-FORMATION RATES For each K-band source (take α = 0.8), L 1.4 (S 1.4, z, α, D L (z)) = π(1 + z) (1+α) D L (z) 2. S 1.4 WHz 1 Linear in S 1.4 but not in L 1.4 ; Condon+ 2002: ( SFR ) M yr 1 = ( L1.4 ) W Hz 1 SSFR SFR M 13

15 SOURCE COUNTS IN M z PLANE (FULL SAMPLE) M_* nsrc z Now assume SSFR is a separable function of M and z (Karim+ 2011): 14

16 SOURCE COUNTS IN M z PLANE (FULL SAMPLE) E M_* nsrc z 10 15

17 SPECIFIC SFR AS A FUNCTION OF STELLAR MASS 16

18 A FEW POINTS Full sample: Agreement with COSMOS work and extend to slightly higher z Disagreement with SAMs (Henriques+ 2012) Simulation underestimating SFR, or AGN contamination..? Ellipticals: Star formation terminates earlier than for starbursts Starbursts: Mass slope independent of z (cf. COSMOS) Irregulars: At roughly same stage of evolution as starbursts at all z probed 17

19 SPECIFIC SFR AS A FUNCTION OF REDSHIFT 18

20 Consistent with downsizing: more massive galaxies formed their stars earlier than less massive ones Full sample: SSFR increases with redshift, but tends to flatten out by and above z 2.5 Starburst sample: SSFR holds up at late times compared to the full sample Ellipticals: star formation in the highest-mass ellipticals does not evolve as fast as for the lowest-mass ones Broadly consistent with results from COSMOS and UKIDSS UDS (NB selection effects) 19

21 EUCLID SKA LONG LIVE STACKING One example of joint analysis of radio data, where infra-red is much deeper Scientific: Take all-sky near infra-red data to 24 mag and radio data to few µjy Let s do this without stacking but then stack to go even deeper Algorithmic: Test strategies for photo-z and M estimation Test SED classification Assess usefulness/applicability of stacking algorithm(s) + extensions 20

22 IN SHORT Have applied radio-stacking technique to K-selected VIDEO galaxies for different subsamples Distinguish objects by (necessarily available) best-fit spectral templates For each, measure SSFR as a function of stellar mass and redshift: Find e.g. SSFR falls with stellar mass (downsizing) Consistent with COSMOS and UKIDSS UDS findings Why not measure SSFR (etc.) behaviour parametrically, directly from data? Make the most of your photons... 21

23 AGN CONTAMINATION Consensus in the literature is that there is no problem: Median stacking would tolerate it Contamination falls rapidly with fainter K (Reddy+ 2005, Daddi+ 2007) AGN fraction falls below 1.4-GHz fluxes of 100 µjy (Fomalont+ 2006, Bondi+ 2007) HDF most of faintest detections resolved so not AGN (Muxlow+ 2005) Simulations agree (Wilman+ 2008, 2010) Only with high-resolution radio imaging to isolate cores or deep-enough X-ray data to eliminate AGN can we really comment 22