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Activity Number: 28 - Computation, Design, and Quality Assurance of Physical Science and Engineering Applications
Type: Contributed
Date/Time: Sunday, August 8, 2021 : 1:30 PM to 3:20 PM
Sponsor: Section on Physical and Engineering Sciences
Abstract #318238
Title: Scalable Statistical Inference of Photometric Redshift via Data Subsampling
Author(s): arindam fadikar* and Stefan M Wild and Jonas Chaves-Montero
Companies: argonne national laboratory and argonne national laboratory and Donostia International Physics Centre
Keywords: data subsampling; photometric redshift; gaussian process

In this talk, we discuss a novel data-driven statistical modeling framework that combines the uncertainties from an ensemble of statistical models learned on smaller subsets of data carefully chosen to account for imbalances in the input space. We demonstrate this method on a photometric redshift estimation problem in cosmology, which seeks to infer a distribution of the redshift -- the stretching effect in observing the light of far-away galaxies -- given multivariate color information observed for an object in the sky. Our proposed method performs balanced partitioning, graph-based data subsampling across the partitions, and training of an ensemble of Gaussian process models.

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

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