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arXiv · 1906.05876

Galaxy-Galaxy Lensing in HSC: Validation Tests and the Impact of Heterogeneous Spectroscopic Training Sets

Joshua S. Speagle·Alexie Leauthaud·Song Huang·Christopher P. Bradshaw·Felipe Ardila·Peter L. Capak·Daniel J. Eisenstein·Daniel C. Masters·Rachel Mandelbaum·Surhud More·Melanie Simet·Cristóbal Sifón
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Abstract

Although photometric redshifts (photo-z's) are crucial ingredients for current and upcoming large-scale surveys, the high-quality spectroscopic redshifts currently available to train, validate, and test them are substantially non-representative in both magnitude and color. We investigate the nature and structure of this bias by tracking how objects from a heterogeneous training sample contribute to photo-z predictions as a function of magnitude and color, and illustrate that the underlying redshift distribution at fixed color can evolve strongly as a function of magnitude. We then test the robustness of the galaxy-galaxy lensing signal in 120 deg$^2$ of HSC-SSP DR1 data to spectroscopic completeness and photo-z biases, and find that their impacts are sub-dominant to current statistical uncertainties. Our methodology provides a framework to investigate how spectroscopic incompleteness can impact photo-z-based weak lensing predictions in future surveys such as LSST and WFIRST.

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Joshua S. Speagle, Alexie Leauthaud, Song Huang, Christopher P. Bradshaw, Felipe Ardila, Peter L. Capak, Daniel J. Eisenstein, Daniel C. Masters, Rachel Mandelbaum, Surhud More, Melanie Simet, Cristóbal Sifón. 2019-06-13. Galaxy-Galaxy Lensing in HSC: Validation Tests and the Impact of Heterogeneous Spectroscopic Training Sets. https://doi.org/10.1093/mnras%2Fstz2968

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