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& Photometric Redshifts with Poisson Noise

Kernel regression ‘correct error’. Quenching Star Formation in Cluster Infall. & Photometric Redshifts with Poisson Noise. Christian Wolf.  z /(1+z) ~ 0.006. 5 Mpc. COMBO-17. 5 Mpc. QE (%). λ / nm. Principal Dataset: COMBO-17 + Spitzer + HST. Hopkins et al. 2006.

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& Photometric Redshifts with Poisson Noise

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

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

  3. Hopkins et al. 2006 Wolf et al. 2003 Bell, Wolf et al. 2004 Faber, Willmer, Wolf et al. 2007 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)

  4. Hopkins et al. 2006 Koopmann & Kenney 2004 Kenney et al. 2008 Wolf et al. 2005, 2009 & press release 2008 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 E Sp S0 frag

  5. Old Red & Dusty Red Galaxies

  6. Aspects of Transformation 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. rms 0.006 rms 0.008 7%-20% outlier Wolf 2001 QE (%) λ/ nm 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)

  9. 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 Poissonnoise… 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 Abdalla et al. 2007 Photometric Redshifts 2010+

  10. 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 The Two Ways of Photo-”Z”eeing *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. 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 The Two Ways of Photo-”Z”eeing • Work continues on • Best templates • Best luminosity functions • Best code debugging • An engineering problemtaken 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 *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…

  12. Kernel regression‘correct error’ Kernel regression‘added error’ ANNz Template fitting Reconstruction of Redshift Distributionsfrom 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. 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? State of the Art vs. Opportunity

  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) 2n(z)  1/Nmodel,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. 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) How to Make It Happen

  16. Summary • 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…??? • 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 • 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. Hamuy et al. 1993/5 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

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