Latent-space field tension for astrophysical component detection

An application to X-ray imaging

¹ Max Planck Institute for Astrophysics, Karl-Schwarzschild-Straße 1, 85748 Garching, Germany
² Ludwig-Maximilians-Universität München, Geschwister-Scholl-Platz 1, 80539 Munich, Germany
³ Kavli Institute for Particle Astrophysics & Cosmology, Stanford University, Stanford, CA 94305, USA
⁴ Excellence Cluster ORIGINS, Boltzmannstraße 2, 85748 Garching, Germany
⁵ Deutsches Zentrum für Astrophysik, Postplatz 1, 02826 Görlitz, Germany

^★ Corresponding author: matteani@mpa-garching.mpg.de

Received: 25 June 2025
Accepted: 24 September 2025

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 multifrequency Bayesian model of the sky emission field that leverages latent-space tension as an indicator of model misspecification, enabling an 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 multifrequency 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 a 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 multiwavelength and multimessenger surveys.