& photometric redshifts with poisson noise christian wolf quenching star formation in cluster...
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&
Photometric Redshiftswith Poisson Noise
Christian Wolf
Quenching Star Formation in Cluster Infall
Kernel regression‘correct error’
z/(1+z) ~ 0.006
λ / nm
QE (
%)
5 Mpc
5 M
pc
Principal Dataset:COMBO-17 + Spitzer + HST
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
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. 2006S0
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Wolf et al. 2005, 2009& press release 2008
Old Red & Dusty Red Galaxies
Aspects of Transformation
Type Blue spiral Red spiral S0 galaxy
SFR-(280-U)rest
clumpiness
(U-V)rest
EB-V stars
EB-V SF
Examples
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
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
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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 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
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…
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
Kernel regression‘added error’
Kernel regression‘correct 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”
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?
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)
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)
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?
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, …
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
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