Astrophysics > Instrumentation and Methods for Astrophysics
arXiv:2110.15209 (astro-ph)
[Submitted on 28 Oct 2021 (v1), last revised 27 Jan 2022 (this version, v2)]
Title:Re-calibrating Photometric Redshift Probability Distributions Using Feature-space Regression
Authors:Biprateep Dey,Jeffrey A. Newman,Brett H. Andrews,Rafael Izbicki,Ann B. Lee,David Zhao,Markus Michael Rau,Alex I. Malz
View a PDF of the paper titled Re-calibrating Photometric Redshift Probability Distributions Using Feature-space Regression, by Biprateep Dey and 7 other authors
View PDFAbstract:Many astrophysical analyses depend on estimates of redshifts (a proxy for distance) determined from photometric (i.e., imaging) data alone. Inaccurate estimates of photometric redshift uncertainties can result in large systematic errors. However, probability distribution outputs from many photometric redshift methods do not follow the frequentist definition of a Probability Density Function (PDF) for redshift -- i.e., the fraction of times the true redshift falls between two limits $z_{1}$ and $z_{2}$ should be equal to the integral of the PDF between these limits. Previous works have used the global distribution of Probability Integral Transform (PIT) values to re-calibrate PDFs, but offsetting inaccuracies in different regions of feature space can conspire to limit the efficacy of the method. We leverage a recently developed regression technique that characterizes the local PIT distribution at any location in feature space to perform a local re-calibration of photometric redshift PDFs. Though we focus on an example from astrophysics, our method can produce PDFs which are calibrated at all locations in feature space for any use case.
| Comments: | Fourth Workshop on Machine Learning and the Physical Sciences (NeurIPS 2021) |
| Subjects: | Instrumentation and Methods for Astrophysics (astro-ph.IM); Machine Learning (cs.LG); Methodology (stat.ME); Machine Learning (stat.ML) |
| Cite as: | arXiv:2110.15209 [astro-ph.IM] |
| (orarXiv:2110.15209v2 [astro-ph.IM] for this version) | |
| https://doi.org/10.48550/arXiv.2110.15209 arXiv-issued DOI via DataCite |
Submission history
From: Biprateep Dey [view email][v1] Thu, 28 Oct 2021 15:27:51 UTC (282 KB)
[v2] Thu, 27 Jan 2022 21:17:04 UTC (282 KB)
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View a PDF of the paper titled Re-calibrating Photometric Redshift Probability Distributions Using Feature-space Regression, by Biprateep Dey and 7 other authors
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