用生成模型解决X射线观测中的堆积效应，提升亮源数据分析精度。
Modeling X-ray photon pile-up with a normalizing flow
- 采用归一化流建模光子堆积的非线性效应，直接处理畸变数据。
- 相比传统方法，后验分布更精确，可利用更多原始观测数据。
- 适用于eROSITA档案数据，为高亮度源研究提供新工具。
X射线天文台搭载的成像探测器动态范围有限，难以覆盖类星体等源的全部通量范围。高入射光子通量导致的光子堆积效应会扭曲实测谱形，造成物理参数推断偏差，极端情况下甚至导致信号完全丢失。由于似然函数难以计算，堆积数据常被丢弃，致使大量档案观测未被充分挖掘。本文提出一种基于模拟的机器学习方法，利用归一化流从堆积的eROSITA数据中估计源参数后验分布。结果表明，该方法生成的后验密度比传统缓解技术更精确，能有效利用更多数据。我们还评估了模型与校准不确定性，并验证了该算法在eROSITA档案真实数据中的适用性。
原文摘要 · Abstract (English)
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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