Statistical Inference for Coadded Astronomical Images

TL;DR

This paper introduces a Bayesian statistical method for analyzing coadded astronomical images, enabling efficient and accurate inference of star locations and fluxes by marginalizing over individual exposures.

Contribution

It presents a novel Bayesian approach that efficiently performs light source inference on coadded images, scaling to large astronomical surveys.

Findings

Outperforms single-exposure trained methods in star localization and flux estimation

Provides a scalable, principled statistical framework for coadded image analysis

Demonstrates effectiveness on simulated astronomical data

Abstract

Coadded astronomical images are created by stacking multiple single-exposure images. Because coadded images are smaller in terms of data size than the single-exposure images they summarize, loading and processing them is less computationally expensive. However, image coaddition introduces additional dependence among pixels, which complicates principled statistical analysis of them. We present a principled Bayesian approach for performing light source parameter inference with coadded astronomical images. Our method implicitly marginalizes over the single-exposure pixel intensities that contribute to the coadded images, giving it the computational efficiency necessary to scale to next-generation astronomical surveys. As a proof of concept, we show that our method for estimating the locations and fluxes of stars using simulated coadds outperforms a method trained on single-exposure images.