{"ID":2841917,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.11863","arxiv_id":"2511.11863","title":"Modeling X-ray photon pile-up with a normalizing flow","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.","short_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, resu...","url_abs":"https://arxiv.org/abs/2511.11863","url_pdf":"https://arxiv.org/pdf/2511.11863v1","authors":"[\"Ole König\",\"Daniela Huppenkothen\",\"Douglas Finkbeiner\",\"Christian Kirsch\",\"Jörn Wilms\",\"Justina R. Yang\",\"James F. Steiner\",\"Juan Rafael Martínez-Galarza\"]","published":"2025-11-14T20:46:32Z","proceeding":"astro-ph.HE","tasks":"[\"astro-ph.HE\",\"astro-ph.IM\",\"cs.LG\"]","methods":"[]","has_code":false}