BibTeX

@article{2511.11863v1,
Author        = {Ole König and Daniela Huppenkothen and Douglas Finkbeiner and Christian Kirsch and Jörn Wilms and Justina R. Yang and James F. Steiner and Juan Rafael Martínez-Galarza},
Title         = {Modeling X-ray photon pile-up with a normalizing flow},
Eprint        = {2511.11863v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.HE},
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.},
Year          = {2025},
Month         = {Nov},
Url           = {http://arxiv.org/abs/2511.11863v1},
File          = {2511.11863v1.pdf}
}