Photometric Redshifts with the LSST: Evaluating Survey Observing Strategies

Abstract

In this paper we present and characterize a nearest-neighbors color-matching photometric redshift estimator that features a direct relationship between the precision and accuracy of the input magnitudes and the output photometric redshifts. This aspect makes our estimator an ideal tool for evaluating the impact of changes to LSST survey parameters that affect the measurement errors of the photometry, which is the main motivation of our work (i.e., it is not intended to provide the "best" photometric redshifts for LSST data). We show how the photometric redshifts will improve with time over the 10-year LSST survey and confirm that the nominal distribution of visits per filter provides the most accurate photo-$z$ results. The LSST survey strategy naturally produces observations over a range of airmass, which offers the opportunity of using an SED- and $z$-dependent atmospheric affect on the observed photometry as a color-independent redshift indicator. We show that measuring this airmass effect and including it as a prior has the potential to improve the photometric redshifts and can ameliorate extreme outliers, but that it will only be adequately measured for the brightest galaxies, which limits its overall impact on LSST photometric redshifts. We furthermore demonstrate how this airmass effect can induce a bias in the photo-$z$ results, and caution against survey strategies that prioritize high-airmass observations for the purpose of improving this prior. Ultimately, we intend for this work to serve as a guide for the expectations and preparations of the LSST science community with regards to the minimum quality of photo-$z$ as the survey progresses.

25 pages, 16 figures, 2 tables, accepted to AJ

References in corpus (2)

• Robust Machine Learning Applied to Astronomical Datasets III: Probabilistic Photometric Redshifts for Galaxies and Quasars in the SDSS and GALEX
• Data-driven, interpretable photometric redshifts trained on heterogeneous and unrepresentative data

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