Recalibrating Photometric Redshift Probability Distributions Using Featurespace Regression
Abstract
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 recalibrate 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 recalibration 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.
 Publication:

arXiv eprints
 Pub Date:
 October 2021
 DOI:
 10.48550/arXiv.2110.15209
 arXiv:
 arXiv:2110.15209
 Bibcode:
 2021arXiv211015209D
 Keywords:

 Astrophysics  Instrumentation and Methods for Astrophysics;
 Computer Science  Machine Learning;
 Statistics  Methodology;
 Statistics  Machine Learning
 EPrint:
 Fourth Workshop on Machine Learning and the Physical Sciences (NeurIPS 2021)