论文标题
使用摊销神经后估计加速的贝叶斯SED建模
Accelerated Bayesian SED Modeling using Amortized Neural Posterior Estimation
论文作者
论文摘要
最先进的光谱能分布（SED）分析使用贝叶斯框架从观察到的光度法或光谱中推断出星系的物理特性。他们需要从高维模型参数的高维空间进行采样，并需要$> 10-100美元的每个银河CPU小时，这使它们几乎不可行，可以分析即将进行的Galaxy Surveys可以观察到的数十亿美元的星系（例如，$ $ desi，$ desi，pfs，pfs，rubin，webb，webb，webb，and webb＆Roman）。在这项工作中，我们提出了一种使用摊销神经后估计（ANPE）的严格贝叶斯推断的替代性可扩展方法。 ANPE是一种基于仿真的推理方法，该方法采用神经网络来估计整个观测范围内的后验概率分布。一旦受过训练，就不需要其他模型评估来估计后部。我们介绍并公开发行，$ {\ rm sed} {flow} $，一种生产最近Hahn等人的后代的ANPE方法。 （2022）来自光学光度法的SED模型。 $ {\ rm sed} {flow} $ take $ {\ sim} 1 $ $ $ $ $秒〜每〜Galaxy $获得12个模型参数的后验分布，所有这些参数与传统的Markov Chain Monte Carlo采样结果都非常一致。我们还将$ {\ rm sed} {flow} $应用于NASA-Sloan Atlas中的33,884个星系，并公开释放他们的后代：请参阅https：//changhoonhahn.githhn.github.io/sedflow。
State-of-the-art spectral energy distribution (SED) analyses use a Bayesian framework to infer the physical properties of galaxies from observed photometry or spectra. They require sampling from a high-dimensional space of SED model parameters and take $>10-100$ CPU hours per galaxy, which renders them practically infeasible for analyzing the $billions$ of galaxies that will be observed by upcoming galaxy surveys ($e.g.$ DESI, PFS, Rubin, Webb, and Roman). In this work, we present an alternative scalable approach to rigorous Bayesian inference using Amortized Neural Posterior Estimation (ANPE). ANPE is a simulation-based inference method that employs neural networks to estimate the posterior probability distribution over the full range of observations. Once trained, it requires no additional model evaluations to estimate the posterior. We present, and publicly release, ${\rm SED}{flow}$, an ANPE method to produce posteriors of the recent Hahn et al. (2022) SED model from optical photometry. ${\rm SED}{flow}$ takes ${\sim}1$ $second~per~galaxy$ to obtain the posterior distributions of 12 model parameters, all of which are in excellent agreement with traditional Markov Chain Monte Carlo sampling results. We also apply ${\rm SED}{flow}$ to 33,884 galaxies in the NASA-Sloan Atlas and publicly release their posteriors: see https://changhoonhahn.github.io/SEDflow.
