Conference Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 416 - Open Problems in Astrostatistics
Type: Topic Contributed
Date/Time: Wednesday, August 10, 2022 : 10:30 AM to 12:20 PM
Sponsor: Section on Physical and Engineering Sciences
Abstract #322803
Title: Calibrated Uncertainty Quantification with Application to Galaxy Photometric Redshifts
Author(s): Ann Lee*
Companies: Carnegie Mellon University
Keywords: conditional density estimation; valid prediction sets; conditional coverage; photometric redshift estimation; uncertainty quantification

Many astrophysical analyses depend on estimates of galaxy redshift (a proxy for distance) determined from photometric (i.e., imaging) data. Photometric redshift uncertainties can result in large systematic errors in down-stream analysis. Many photometric redshift methods aim to accurately estimate uncertainties; the output can be treated as "photo-z" estimates of the probability density of redshift z given photometric data x, or p(z|x). Open problems are how best to assess the accuracy of these conditional density estimates, and how to use diagnostics to improve estimates of p(z|x). In this talk, I will describe a new statistical framework for assessing conditional density estimates and empirical conditional coverage of prediction sets. The approach provides easy-to-interpret diagnostics of modes of failure, together with practical procedures for recalibrating conditional density estimates and prediction sets to achieve approximate finite-sample coverage. We illustrate our diagnostics and recalibration approach on galaxy photo-z estimation, as well as hurricane intensity estimation based on sequences of satellite image data.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2022 program