2020/01/06 by J. Bryce Kalmbach, Jacob T. VanderPlas, Andrew J. Connolly
Computer Science · Mathematics · Physics and Astronomy · #Astronomy and Astrophysical Research #Estimator #Galaxies: Formation, Evolution, Phenomena #Large Synoptic Survey Telescope #Outlier #Photometric redshift #Redshift #Standard deviation #Stellar, planetary, and galactic studies #Weak gravitational lensing #astro-ph.IM #cs.IT #math.IT #stat.AP
paper · pdf · doi:10.3847/1538-4357/ab684f
29 pages, 17 figures, accepted to ApJ
arxiv created 2020/01/06 · openalex created_date 2020/01/10 · openalex publication_date 2020/02/10 · arxiv updated 2021/01/27 · openalex updated_date 2026/08/05
Abstract In this paper we apply ideas from information theory to create a method for the design of optimal filters for photometric redshift estimation. We show the method applied to a series of simple example filters in order to motivate an intuition for how photometric redshift estimators respond to the properties of photometric passbands. We then design a realistic set of six filters covering optical wavelengths that optimize photometric redshifts for z <= 2.3 and i < 25.3. We create a simulated catalog for these optimal filters and use our filters with a photometric redshift estimation code to show that we can improve the standard deviation of the photometric redshift error by 7.1% overall and improve outliers 9.9% over the standard filters proposed for the Large Synoptic Survey Telescope (LSST). We compare features of our optimal filters to the LSST and find that the LSST filters incorporate key features for optimal photometric redshift estimation. Finally, we describe how information theory can be applied to a range of optimization problems in astronomy.