complete calibration of the color-redshift relation (c3r2 ...george/ay127/stern_c3r2.pdfthat only...
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Complete Calibration of the Color-Redshift Relation (C3R2): Keck Spectroscopy to Train Photometric Redshifts for Euclid and WFIRST
Daniel Stern (JPL/Caltech)on behalf of the C3R2 Team
2017 January 5AAS#229 - Grapevine, Texas
C3R2 = Complete Calibration of the Color-Redshift Relation
Daniel Stern (JPL) - PI of NASA Keck C3R2 allocation10 nights allocated (all DEIMOS)
Judith Cohen (Caltech) - PI of Caltech Keck C3R2 allocation 16 nights allocated (DEIMOS + LRIS + MOSFIRE)Dave Sanders (IfA) - PI of Univ. of Hawaii Keck C3R2 allocation 6 nights allocated (all DEIMOS)Bahram Mobasher (UC-Riverside) - PI of UC Keck C3R2 allocation 2.5 nights allocated (all DEIMOS)
+ time allocations on VLT, Magellan, and GCT
Peter Capak (IPAC)Daniel Masters (IPAC)S. Adam Stanford (UC-Davis/Livermore)+ Euclid Photometric Redshift Organizational Unit (OU-PHZ)
Spectroscopic Needs for Euclid & WFIRST
• the next generation of weak lensing (WL) experiments will require significantly improved photometric redshifts in order to achieve their cosmological goals.
• significant work in the community has attempted to identify the required/optimal spectroscopic samples to train/validate these next generation photometric redshifts.
• Masters, Capak, Stern et al. (2015) presents an approach using the machine-learning “Self Organizing Map” (SOM) methodology (see next slide). Significant interest/excitement within the community that this approach helps define the optimal minimal spectroscopic sample: specifically, the SOM optimally groups galaxies by their multi-dimensional colors, identifying galaxies with very similar colors, which correspond to similar redshifts. The SOM also flags outliers, problematic colors/cells, and the density of galaxies of each color/cell.
• Masters et al. (2015) includes an analysis of the SOM-based needs for Euclid, showing that only ~50% of SOM cells currently have high-quality training spectroscopic redshifts, but that a ~40 night Keck multi-object spectroscopic program would bring that level to ~95%, thus fulfilling the photometric redshift training requirements for Euclid, and making a significant downpayment for WFIRST.
Spectroscopic Needs for Euclid & WFIRST
Example SOM for Euclid, based on COSMOS
Masters et al. (2015; ApJ, 813, 1)
Example SOM for Euclid, based on COSMOS
Masters et al. (2015; ApJ, 813, 1)
• colored by cell occupation
Example SOM for Euclid, based on COSMOS
Masters et al. (2015; ApJ, 813, 1)
u-g g-r
Example SOM for Euclid, based on COSMOS
Masters et al. (2015; ApJ, 813, 1)
• colored by median i-band magnitude in each cell
Example SOM for Euclid, based on COSMOS
Masters et al. (2015; ApJ, 813, 1)
8-band photo-z 30-band photo-z
Example SOM for Euclid, based on COSMOS
Masters et al. (2015; ApJ, 813, 1)
• colored by dispersion in photo-z’s for each cell: greatest dispersion at transitions regions between low-redshift and high-redshift galaxies
• the next generation of weak lensing (WL) experiments will require significantly improved photometric redshifts in order to achieve their cosmological goals.
• significant work in the community has attempted to identify the required/optimal spectroscopic samples to train/validate these next generation photometric redshifts.
• Masters, Capak, Stern et al. (2015) presents an approach using the machine-learning “Self Organizing Map” (SOM) methodology (see next slide). Significant interest/excitement within the community that this approach helps define the optimal minimal spectroscopic sample: specifically, the SOM optimally groups galaxies by their multi-dimensional colors, identifying galaxies with very similar colors, which correspond to similar redshifts. The SOM also flags outliers, problematic colors/cells, and the density of galaxies of each color/cell.
• Masters et al. (2015) includes an analysis of the SOM-based needs for Euclid, showing that only ~50% of SOM cells currently have high-quality training spectroscopic redshifts, but that a ~40 night Keck multi-object spectroscopic program would bring that level to ~95%, thus fulfilling the photometric redshift training requirements for Euclid, and making a significant downpayment for WFIRST.
Spectroscopic Needs for Euclid & WFIRST
2016A results
• results from first 5 nights of this program (all Caltech nights; 3 DEIMOS, 1 LRIS, 1 MOSFIRE)
• only showing “Quality 1” = grade A redshifts (i.e., high reliable, based on multiple features; >99% reliable): ~1050 quality 1 redshifts + ~300 quality 2 redshifts (i.e., likely, but typically based on a single feature; ~95% reliable)
• first data release and associated paper planned for submission in February (Masters et al., in prep.)
Median spec-z, high-confidence redshifts
0 20 40 600
20
40
60
80
100
120
140
0 1 2 3 4 5 6
BEFORE
2016A results
• results from first 5 nights of this program (all Caltech nights; 3 DEIMOS, 1 LRIS, 1 MOSFIRE)
• only showing “Quality 1” = grade A redshifts (i.e., high reliable, based on multiple features; >99% reliable): ~1050 quality 1 redshifts + ~300 quality 2 redshifts (i.e., likely, but typically based on a single feature; ~95% reliable)
• first data release and associated paper planned for submission in February (Masters et al., in prep.)
Median spec-z, high-confidence redshifts
0 20 40 600
20
40
60
80
100
120
140
0 1 2 3 4 5 6
AFTER
0 0.5 1 1.5 2 2.5 3 3.5 4Redshift
0
0.05
0.1
0.15
0.2
0.25
Frac
tion
of S
ampl
e
Total SampleRedshifts in 2016A
2016A results
2016A results
2016A results
Dec. 2015UDS mask 1, night 1slit#80 (DEIMOS)