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.

翻译：黑洞X射线双星可通过光谱拟合进行研究，从而为极端引力环境下的吸积过程提供物理约束。传统的光谱拟合方法（如马尔可夫链蒙特卡罗方法）因计算时间限制而面临瓶颈。本文提出一种概率模型，该模型采用结合归一化流的变分自编码器进行训练，以构建具有物理意义的隐空间。该神经网络能够预测光谱模型参数及其完整的概率分布。我们的实现方案在光谱重建效果上较先前的确定性模型有显著提升，同时计算速度比传统方法快三个数量级。