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)