带有归一化流的变分自编码器用于X射线光谱拟合
Variational Autoencoder with Normalizing flow for X-ray spectral fitting
AI总结：
本文提出一种基于变分自编码器和归一化流的模型，用于高效X射线光谱拟合，实现快速且精确的光谱重建。
摘要：
通过光谱拟合研究黑洞X射线双星（BHBs）可以为极端引力环境中的吸积过程提供物理约束。传统光谱拟合方法如马尔可夫链蒙特卡罗（MCMC）由于计算时间限制而面临挑战。我们引入了一种概率模型，利用带有归一化流的变分自编码器，训练以采用物理潜在空间。该神经网络能够产生光谱模型参数的预测及其完整概率分布。我们的实现结果在光谱重建方面显著优于之前的确定性模型，同时比传统方法快三个数量级。
英文摘要：
Black hole X-ray binaries (BHBs) can be studied with spectral fitting to provide physical constraints on accretion in extreme gravitational environments. Traditional methods of spectral fitting such as Markov Chain Monte Carlo (MCMC) face limitations due to computational times. We introduce a probabilistic model, utilizing a variational autoencoder with a normalizing flow, trained to adopt a physical latent space. This neural network produces predictions for spectral-model parameters as well as their full probability distributions. Our implementations result in a significant improvement in spectral reconstructions over a previous deterministic model while performing three orders of magnitude faster than traditional methods.
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