Scalable Statistical Inference of Photometric Redshift via Data Subsampling

TL;DR

This paper introduces a scalable statistical inference framework for photometric redshift estimation that combines ensemble modeling on data subsets with graph-based sampling to handle large cosmological datasets effectively.

Contribution

It presents a novel data-driven approach that integrates ensemble Gaussian process models with balanced data partitioning and graph-based subsampling for scalable uncertainty quantification.

Findings

Effective in handling large datasets in cosmology

Provides accurate uncertainty estimates for redshift predictions

Outperforms traditional models in scalability and uncertainty quantification

Abstract

Handling big data has largely been a major bottleneck in traditional statistical models. Consequently, when accurate point prediction is the primary target, machine learning models are often preferred over their statistical counterparts for bigger problems. But full probabilistic statistical models often outperform other models in quantifying uncertainties associated with model predictions. We develop a 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.