Latent-space Field Tension for Astrophysical Component Detection An application to X-ray imaging

TL;DR

This paper introduces a Bayesian model leveraging latent-space tension to automatically separate and reconstruct astrophysical components in multi-frequency X-ray data, improving accuracy and uncertainty quantification.

Contribution

It presents a novel latent-space tension-based Bayesian framework for astrophysical component separation, guiding model refinement and diagnostics in multi-wavelength data analysis.

Findings

Accurate reconstruction of astrophysical components in synthetic data.

High-precision localization of point sources in X-ray observations.

Robust separation of diffuse and extended emissions with uncertainty estimates.

Abstract

Modern observatories are designed to deliver increasingly detailed views of astrophysical signals. To fully realize the potential of these observations, principled data-analysis methods are required to effectively separate and reconstruct the underlying astrophysical components from data corrupted by noise and instrumental effects. In this work, we introduce a novel multi-frequency Bayesian model of the sky emission field that leverages latent-space tension as an indicator of model misspecification, enabling automated separation of diffuse, point-like, and extended astrophysical emission components across wavelength bands. Deviations from latent-space prior expectations are used as diagnostics for model misspecification, thus systematically guiding the introduction of new sky components, such as point-like and extended sources. We demonstrate the effectiveness of this method on synthetic multi-frequency imaging data and apply it to observational X-ray data from the eROSITA Early Data Release (EDR) of the SN1987A region in the Large Magellanic Cloud (LMC). Our results highlight the method's capability to reconstruct astrophysical components with high accuracy, achieving sub-pixel localization of point sources, robust separation of extended emission, and detailed uncertainty quantification. The developed methodology offers a general and well-founded framework applicable to a wide variety of astronomical datasets, and is therefore well suited to support the analysis needs of next-generation multi-wavelength and multi-messenger surveys.