论文标题
Desi Propabgs的神经恒星种群合成模拟器
Neural Stellar Population Synthesis Emulator for the DESI PROVABGS
论文作者
论文摘要
概率增值的明亮星系调查（Propabgs）目录将提供物理性能的后验分布，$> 1000万美元的DESI Bright Galaxy Survey（BGS）星系。使用马尔可夫链蒙特卡洛采样和[arxiv：2202.01809]恒星种群合成（SPS）模型，将从观察到的光度法和光谱的联合贝叶斯建模来推断每个后验分布。为了使这种计算可行，Propabgs将使用神经模拟器进行SPS模型来加速后推断。在这项工作中，我们介绍了如何使用[Arxiv：1911.11778]方法构造模拟器，并验证它可以用于准确推断出星系性能。我们确认，仿真器与具有$ \ ll 1 \％$错误的原始SPS型号非常一致，并且$ 100 \ times $更快。此外，我们证明了使用仿真器得出的星系性能的后代与使用原始模型推断的人非常吻合。这项工作中介绍的神经模拟器对于绕过构建Provabgs目录所带来的计算挑战至关重要。此外，它证明了模仿将复杂分析扩展到数百万星系的优势。
The Probabilistic Value-Added Bright Galaxy Survey (PROVABGS) catalog will provide the posterior distributions of physical properties of $>10$ million DESI Bright Galaxy Survey (BGS) galaxies. Each posterior distribution will be inferred from joint Bayesian modeling of observed photometry and spectroscopy using Markov Chain Monte Carlo sampling and the [arXiv:2202.01809] stellar population synthesis (SPS) model. To make this computationally feasible, PROVABGS will use a neural emulator for the SPS model to accelerate the posterior inference. In this work, we present how we construct the emulator using the [arXiv:1911.11778] approach and verify that it can be used to accurately infer galaxy properties. We confirm that the emulator is in excellent agreement with the original SPS model with $\ll 1\%$ error and is $100\times$ faster. In addition, we demonstrate that the posteriors of galaxy properties derived using the emulator are also in excellent agreement with those inferred using the original model. The neural emulator presented in this work is essential in bypassing the computational challenge posed in constructing the PROVABGS catalog. Furthermore, it demonstrates the advantages of emulation for scaling sophisticated analyses to millions of galaxies.
