1. & Photometric Redshifts with Poisson Noise Christian Wolf Quenching Star Formation in Cluster Infall Kernel regression ‘correct error’

2.  z /(1+z) ~ 0.006 λ / nm QE (%) 5 Mpc Principal Dataset: COMBO-17 + Spitzer + HST

3. I. Declining Star Formation COMBO-17 covered redshift 0..1 Mapped luminosity evolution of galaxy population Build-up of stellar mass in red sequence COMBO-17 photo-z and spectroscopic surveys get identical results SF decline *not* driven by mergers (Wolf et al. 2005) Hopkins et al. 2006 Faber, Willmer, Wolf et al. 2007Bell, Wolf et al. 2004Wolf et al. 2003

4. Declining Star Formation with Density COMBO-17 studied A901ab/902 (z=0.17) Dusty red galaxies = red spirals, see transformation in action, ongoing SF across the “gap” Slow quenching of star formation, *not* driven by mergers or starburst Hopkins et al. 2006 S0 Sp E frag Koopmann & Kenney 2004 Kenney et al. 2008 Wolf et al. 2005, 2009 & press release 2008

5. Old Red & Dusty Red Galaxies

6. Aspects of Transformation TypeBlue spiralRed spiralS0 galaxy SFR -(280-U) rest clumpiness (U-V) rest E B-V stars E B-V SF Examples

7. Physics of Star Formation Decline? Discussed origins of SF decline –Merger-triggered starbursts & exhaustion –Ram-pressure stripping of cold gas –Starvation of cold gas supply –Tidal effects from cluster potential –Galaxy-galaxy interactions –Gravitational heating in structure formation* Approaches –Detailed physical models challenging –Need better account of what’s there Are S0s made from present spirals? –Disk fading, light profiles, central SF –Outside-in truncation of SF disk, dependence on cluster? –Cluster infall time scales from SFH templates in fossil records Trends with redshift? –Higher-redshift clusters in comparison –Expect inversion of SF-density relation… Future observations –H  imaging of A901/2 (OSIRIS via ESO) –Colour gradients in SDSS group galaxies –AO-assisted IFU study of intermediate-z galaxies (SWIFT@Palomar) –Analysis of HIROCS clusters z~0.6 to 1.6 Long-term approaches –AO & MCAO-assisted line imaging and IFU spectroscopy of z~1..2 clusters (ESO VLT: MUSE & post-MAD devices) –HST-based line imaging with WF3 NIR (J-band) narrow-bands of z~1 clusters

8. II. Photometric Redshifts and Classification COMBO-17 –Many narrow bands –Template-based classification  Classes galaxy, star, qso, wd  Reaches  z /(1+z) < 0.01  Accurate rest-frame SED (and old red : dusty red) λ / nm QE (%) Wolf 2001 rms 0.008 7%-20% outlier rms 0.006

9. Abdalla et al. 2007 Photometric Redshifts 2010+ Trends –Before CADIS & COMBO-17 little science published from photo-z (only “experimentation”) –Scientific value demonstrated! –Now: Strong drive towards huge / all-sky surveys: Dark Energy Survey, PanStarrs VST, VISTA, LSST, IDEM … –Beat Poisson noise… –But: impact of systematics?! Applications –Extremely large stand-alone photo-z surveys –Search for interesting spectroscopic targets To learn about –Cosmology & dark energy via BAO and gravitational lensing –Galaxy growth embedded in dark matter haloes –Galaxy evolution over time and with environment –New phenomena

10. The Two Ways of Photo-”Z”eeing Model input: –Educated guesses –I.e. template SEDs and for priors evolving luminosity functions etc. Photo-z output: –Educated guesses* –“What could be there? What is it probably not?” Needed for –Pioneering new frontier: depth in magnitude or z beyond spectroscopic reach Model input: –Statistical Distributions –I.e. empirically measured n(z) distributions for all locations in flux space Photo-z output: –Statistical Distributions –“What is there? And in which proportions?” Needed for –Extrapolating to wide area: known mag/z territory with spectroscopic description *You will never obtain a reliable estimate of an n(z) distribution from a technique that involves more than zero pieces of information which do not represent a true distribution but a best-guess approximation instead…

11. The Two Ways of Photo-”Z”eeing Model input: –Educated guesses –I.e. template SEDs and for priors evolving luminosity functions etc. Photo-z output: –Educated guesses* –“What could be there? What is it probably not?” Needed for –Pioneering new frontier: depth in magnitude or z beyond spectroscopic reach Model input: –Statistical Distributions –I.e. empirically measured n(z) distributions for all locations in flux space Photo-z output: –Statistical Distributions –“What is there? And in which proportions?” Needed for –Extrapolating to wide area: known mag/z territory with spectroscopic description *You will never obtain a reliable estimate of an n(z) distribution from a technique that involves more than zero pieces of information which do not represent a true distribution but a best-guess approximation instead… Work continues on –Best templates –Best luminosity functions –Best code debugging –An engineering problem taken care of by PHAT Work continues on –How to get n(z) correctly in presence of errors? Problem solved: –Wolf (2009), MN in press –n(z) with Poisson-only errors

12. Kernel regression ‘added error’ Kernel regression ‘correct error’ ANNzTemplate fitting Reconstruction of Redshift Distributions from ugriz-Photometry of QSOs Left panel: The n(z) of a QSO sample is reconstructed with errors of 1.08  Poisson noise (works with any subsample or individual objects as well) using “  2 -testing with noisy models”

13. State of the Art vs. Opportunity Current methods –Precision sufficient for pre-2010 science –But insufficient and critical for the next decade Current surveys –VVDS and DEEP-2: 20%..50% incomplete at 22..24 mag –SDSS (bright survey) 3…4%? Propagates into bias –  non-recov =0.2 (20% incomplete)  |   z  |=0.2 ! and  w ~ 1 ! Poisson-precision results need  Adoption of Wolf 2009 method removes methodical limitations  Complete(!) “training set” removes data limitations –Incompleteness  systematics Issues –Lines in the NIR, weak lines at low metallicity, what else? –Goal 1..5% incompleteness –Provide sub-samples with <1% incompleteness –Fundamental limits? Blending?

14. Vision: The PRATER Project “Photometric Redshift Archive for Training External Resources” –Gaussian-precision photo-z code & residual risk quantification from model incompleteness/size moves all attention to model –Residual outlier risk   incomplete –Noise floor on n(z)  2 n(z)  1/N model,z-bin –Best model is (i) most complete and (ii) largest Build: a world reference repository for post-2010 photo-zeeing –Best-possible photo-z quality by design (method, completeness, size) –Quality limits understood and communicated quantitatively –Dynamic and growing with time –Customers submit small “photo-zeeing” jobs over the web or ask for mirror installation in large applications (e.g. PanStarrs)

15. How to Make It Happen Collaboration with critical mass, expertise & telescope access –O. LeFevre, Marseille (VIMOS) –J. Newman, S. Cruz (DEEP-2) –T. Shanks, Durham (XMS) –P. Prada, Granada (SIDE/GTC) –G. Dalton, Oxford (FMOS) Clarify incompleteness sources Coordinate multi-instrument plan to gather required data –VIMOS upgrade at ESO-VLT –FMOS (NIR) at Subaru 2010 –XMS at Calar Alto 2015? –SIDE at GTC 2015? First demonstrator: –Using zCOSMOS for SDSS 5-year goal: –Ingest best-possible VIMOS & FMOS completeness –Perfect performance to 23 mag Drive next-gen instrumentation –Supported by ESA/ESO Report on Fundamental Cosmology –Dark Energy Task Force (U.S.) 10-year goal: –Reach as deep as possible with newest spectrographs, possibly space-based post-2020 (IDEM)

16. Summary I.Cosmic decline in star formation Over time and environment: not(!) driven by major mergers Star formation fading in cluster infall makes S0-like galaxies Interplay of fading process with structure formation…??? II.Photo-z’s or n(z|c) for future surveys Method devised for Poisson-precision results (if model complete) Completeness of deep spectroscopy is primary quality limit Window of opportunity: PRATER project as world reference Community concerned and supportive, considering instrumentation III.Stellar explosions …see avalanche in available data, but follow-up and interpretation? Dark energy from SNe Ia: extinction correction dubious, metallicity?

17. III. Stellar Explosions Host galaxies SNe II : L-GRBs –If GRB Z * cut-off, then solar Cosmic SFR : SNR history –SN Ia delay time 0-4 Gyr Archival event search –GALEX UV flash before SN II  red giant progenitor –Radio: BBC, SWR, …

18. Stellar Explosions: Work 2010+ Ages & Z * of SN subtypes –Links to progenitor evolution –Galaxy proxy, residual trend SN Ia light curves: 3-p family –Host galaxy age, dust & Z * ? –Cosmic calibration drift? Early light curves & flashes –Stacking, short cadence, SDSS, PTF, SkyMapper, Calan 2010+ –Explosion conditions Conley et al. 2008 Hamuy et al. 1993/5