Modeling X-ray photon pile-up with a normalizing flow

• URL: http://arxiv.org/abs/2511.11863v1
• Date: Fri, 14 Nov 2025 20:46:32 GMT
• Title: Modeling X-ray photon pile-up with a normalizing flow
• Authors: Ole König, Daniela Huppenkothen, Douglas Finkbeiner, Christian Kirsch, Jörn Wilms, Justina R. Yang, James F. Steiner, Juan Rafael Martínez-Galarza,
• Abstract summary: The analysis of bright X-ray sources is complicated by so-called pile-up, which results from high incident photon flux.<n>We present a machine learning solution to this problem, using a simulation-based inference framework that allows us to estimate posterior distributions of physical source parameters from piled-up eROSITA data.<n>We show that a normalizing flow produces better-constrained posterior densities than traditional mitigation techniques, as more data can be leveraged.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The dynamic range of imaging detectors flown on-board X-ray observatories often only covers a limited flux range of extrasolar X-ray sources. The analysis of bright X-ray sources is complicated by so-called pile-up, which results from high incident photon flux. This nonlinear effect distorts the measured spectrum, resulting in biases in the inferred physical parameters, and can even lead to a complete signal loss in extreme cases. Piled-up data are commonly discarded due to resulting intractability of the likelihood. As a result, a large number of archival observations remain underexplored. We present a machine learning solution to this problem, using a simulation-based inference framework that allows us to estimate posterior distributions of physical source parameters from piled-up eROSITA data. We show that a normalizing flow produces better-constrained posterior densities than traditional mitigation techniques, as more data can be leveraged. We consider model- and calibration-dependent uncertainties and the applicability of such an algorithm to real data in the eROSITA archive.

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