用流形变分自编码器加速黑洞吸积谱拟合，快3000倍且更准。
Variational Autoencoder with Normalizing flow for X-ray spectral fitting
- 用带归一化流的变分自编码器学习物理意义的潜在空间
- 重建精度优于旧模型，推理速度比传统方法快1000倍以上
- 适合需要快速高精度光谱分析的天体物理研究者
黑洞X射线双星（BHBs）可通过谱拟合获得极端引力环境下吸积过程的物理约束。传统谱拟合方法如马尔可夫链蒙特卡洛（MCMC）受限于计算耗时。本文提出一种概率模型，采用带有归一化流的变分自编码器，并训练其在具有物理意义的潜在空间中进行表示。该神经网络不仅能预测谱模型参数，还能输出其完整概率分布。实验表明，该方法在谱重建精度上显著优于先前的确定性模型，且推理速度比传统方法快三个数量级。
原文摘要 · Abstract (English)
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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