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Transcript of & Photometric Redshifts with Poisson Noise Christian Wolf Quenching Star Formation in Cluster...

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  • & Photometric Redshifts with Poisson Noise Christian Wolf Quenching Star Formation in Cluster Infall Kernel regression correct error
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  • z /(1+z) ~ 0.006 / nm QE (%) 5 Mpc Principal Dataset: COMBO-17 + Spitzer + HST
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  • 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
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  • 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
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  • Old Red & Dusty Red Galaxies
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  • Aspects of Transformation TypeBlue spiralRed spiralS0 galaxy SFR -(280-U) rest clumpiness (U-V) rest E B-V stars E B-V SF Examples
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  • 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 whats 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
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  • 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) / nm QE (%) Wolf 2001 rms 0.008 7%-20% outlier rms 0.006
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  • 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
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  • The Two Ways of Photo-Zeeing 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
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  • The Two Ways of Photo-Zeeing 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
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  • 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
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  • 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) 34%? 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