用神经场建模X射线光子到达的泊松过程，实现无监督特征提取与率函数重建。
A Poisson Process AutoDecoder for X-ray Sources
- 将泊松过程建模为连续率函数，通过隐变量解码实现无监督学习
- 在钱德拉源目录上实现高精度率函数重构与多任务性能提升
- 适合天体物理中异常检测、分类等需要精确时间-能量建模的任务
X射线观测设施如钱德拉望远镜和eROSITA已探测到数百万与高能现象相关的天体源。光子到达时间遵循泊松过程，且速率变化范围可达数个数量级，给源分类、物理参数推导和异常检测带来挑战。以往方法或未能直接捕捉数据的泊松特性，或仅聚焦于泊松率函数重建。本文提出泊松过程自编码器（PPAD），一种神经场解码器，通过无监督学习将固定长度的隐变量映射为跨能区与时间的连续泊松率函数。PPAD同时完成率函数重建与表示学习。我们在钱德拉源目录上通过重构、回归、分类和异常检测实验验证了其有效性。
原文摘要 · Abstract (English)
X-ray observing facilities, such as the Chandra X-ray Observatory and the eROSITA, have detected millions of astronomical sources associated with high-energy phenomena. The arrival of photons as a function of time follows a Poisson process and can vary by orders-of-magnitude, presenting obstacles for common tasks such as source classification, physical property derivation, and anomaly detection. Previous work has either failed to directly capture the Poisson nature of the data or only focuses on Poisson rate function reconstruction. In this work, we present Poisson Process AutoDecoder (PPAD). PPAD is a neural field decoder that maps fixed-length latent features to continuous Poisson rate functions across energy band and time via unsupervised learning. PPAD reconstructs the rate function and yields a representation at the same time. We demonstrate the efficacy of PPAD via reconstruction, regression, classification and anomaly detection experiments using the Chandra Source Catalog.
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