Modeling X-ray photon pile-up with a normalizing flow
Fuente:
arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911266556084224 |
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| author | König, Ole Huppenkothen, Daniela Finkbeiner, Douglas Kirsch, Christian Wilms, Jörn Yang, Justina R. Steiner, James F. Martínez-Galarza, Juan Rafael |
| author_facet | König, Ole Huppenkothen, Daniela Finkbeiner, Douglas Kirsch, Christian Wilms, Jörn Yang, Justina R. Steiner, James F. Martínez-Galarza, Juan Rafael |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_11863 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Modeling X-ray photon pile-up with a normalizing flow König, Ole Huppenkothen, Daniela Finkbeiner, Douglas Kirsch, Christian Wilms, Jörn Yang, Justina R. Steiner, James F. Martínez-Galarza, Juan Rafael High Energy Astrophysical Phenomena Instrumentation and Methods for Astrophysics Machine Learning 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. |
| title | Modeling X-ray photon pile-up with a normalizing flow |
| topic | High Energy Astrophysical Phenomena Instrumentation and Methods for Astrophysics Machine Learning |
| url | https://arxiv.org/abs/2511.11863 |