LSST Observing Strategies and Photometric Redshifts Melissa Graham - - PowerPoint PPT Presentation

Melissa Graham

LSST Observing Strategies and Photometric Redshifts

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How does it work? For every test set galaxy, calculate the Mahalanobis distance in color-space to every training set galaxy.

𝑑 = color 𝜀𝑑 = error in color

(Where color is the difference in brightness between adjacent filters: u-g, g-r, r-i, i-z, z-y.)

Step 1 Step 2

Apply a threshold on DM to identify the subset of training galaxies that are “well-matched” in color-space. Choose 1 color-matched training-set galaxy and use its “true” redshift as the photo-z for the test galaxy.

galaxies with “true” (spectroscopic) redshift galaxies for which zphot is estimated

Training Set: Test Set:

Graham, Connolly, Ivezić, Schmidt et al. (2018)

CMNN: Color-Matched Nearest-Neighbors

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galaxies with “true” (spectroscopic) redshift galaxies for which zphot is estimated

Training Set: Test Set:

Simulated photometry scattered by predicted observational errors.

CMNN: Color-Matched Nearest-Neighbors

Graham, Connolly, Ivezić, Schmidt et al. (2018)

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How will the photo-z improve as the LSST photometric uncertainties decrease over the 10 year survey?

Arrows mark year when SRD values met.

CMNN: Color-Matched Nearest-Neighbors

Graham, Connolly, Ivezić, Schmidt et al. (2018)

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Graham et al. 2018 also considers: — potential changes to the LSST observing strategy, such as the distribution of visits per filter —> e.g., less u- or y-band, gri only during year 1 — impact of general coefficients to, and systematic

• ffsets, on the photometric errors

— consequences of a mismatch in color or redshift distributions between test and training sets — whether a subtle change in effective filter transmission due to airmass can be used to refine photo-z estimates

CMNN: Color-Matched Nearest-Neighbors

Graham, Connolly, Ivezić, Schmidt et al. (2018)

CMNN: Impacts of NIR/NUV Photometry

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Work In Progress

Paper nearly ready showing simulations of LSST photo-z results when photometry from:

• ESA Euclid (NIR)
• NASA WFIRST (NIR)
• CSA CASTOR (NUV)

are included in the simulation.

Cosmological Advanced Survey Telescope for Optical and UV Research

Graham, Connolly, et al. (2018b, in prep.)

CMNN: Impacts of NIR/NUV Photometry

Euclid: impact within the overlapping area (LSST + Euclid)

The paper also evaluates:

• results considering Euclid is 40% of the total WFD area
• results in a shallow Northern LSST survey to extend Euclid overlap
• results with a deeper training set built from overlapping DDF

Graham, Connolly, et al. (2018b, in prep.)

CMNN: Impacts of NIR/NUV Photometry

WFIRST: impact within the overlapping area (~2000 sq.deg.)

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The paper also evaluates:

• results with only WFIRST YJHK
• results for a deeper test set of i<26.8 mag

Graham, Connolly, et al. (2018b, in prep.)

CMNN: Impacts of NIR/NUV Photometry

CASTOR: impact within the overlapping area (~6000 sq.deg.)

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The paper also evaluates:

• results with CASTOR + Euclid in the 6000 sq.deg
• results with CASTOR + WFIRST in the southern 2000 sq.deg.

Graham, Connolly, et al. (2018b, in prep.)

CMNN: Results from OpSim Runs

Photometric Depth Evolution of OpSim Runs

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Divide the OpSim runs into three categories: (1) enlarge the total survey area, (2) change the survey’s visits, and (3) do a rolling cadence. Plot median depth of extragalactic WFD fields with >3 filters vs. year.

CMNN: Results from OpSim Runs

Photometric Depth Evolution of OpSim Runs

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Divide the OpSim runs into three categories: (1) enlarge the total survey area, (2) change the survey’s visits, and (3) do a rolling cadence. Plot median depth of extragalactic WFD fields with >3 filters vs. year.

CMNN: Results from OpSim Runs

Photometric Depth Evolution of OpSim Runs

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Divide the OpSim runs into three categories: (1) enlarge the total survey area, (2) change the survey’s visits, and (3) do a rolling cadence. Plot median depth of extragalactic WFD fields with >3 filters vs. year.

CMNN: Results from OpSim Runs

Photo-z Quality Evolution for 3 OpSim Runs (1 per Category)

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Compare the statistical quality of photo-z in extragalactic WFD fields to the baseline, as a function of zphot, for each year. PanSTARRS-like area; 40/20s u/grizy visits; two 𝜀-band roll

CMNN: Results from OpSim Runs

Photo-z Quality Evolution for 3 OpSim Runs (1 per Category)

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Compare the statistical quality of photo-z in extragalactic WFD fields to the baseline, as a function of zphot, for each year. PanSTARRS-like area; 40/20s u/grizy visits; two 𝜀-band roll

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The CMNN code is for evaluating the relative photo-z quality between observing strategies that change the photometry. Documents exist that quantify the effects

• n photo-z from changes to the observing

strategy, and overlap with NIR/NUV surveys. I’m happy to take requests for photo-z quality simulations that would help evaluate proposed cadences for white papers.

Melissa Graham, mlg3k@uw.edu

Photo-z results can be point estimates, full posteriors, or bulk statistical measures (standard deviation, bias, fraction of outliers).

CMNN: Summary