基于神经后验估计的星系光谱深度推断

IF 6.3 2区 物理与天体物理 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
G. Khullar, B. Nord, A. Ćiprijanović, J. Poh, Fei Xu
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引用次数: 3

摘要

随着数十亿个星系的复杂数据调查的出现,现在需要的是用强大的不确定性量化来有效地模拟星系光谱能量分布(SED)。基于模拟推理(SBI)和摊余神经后验估计(NPE)的结合已成功地用于精确有效地分析模拟和真实星系的光度。在这项工作中,我们利用这种组合,并在现有文献的基础上分析模拟的有噪声的星系光谱。在这里,我们展示了光谱的概念验证研究,即(a)对星系SED的有效分析和对具有物理可解释不确定性的星系参数的推断;和(b)用马尔可夫链蒙特卡罗(MCMC)方法以几个星系拟合的适度代价对所述星系参数的后验分布进行摊销计算。我们利用SED生成器和推理框架“浏览”生成模拟光谱,并使用NPE训练2×106光谱的数据集(对应于五参数SED模型)。我们证明,SBI——结合了快速和摊销后验估计——能够推断出准确的星系恒星质量和金属含量。我们的不确定性约束与贝叶斯MCMC方法的传统逆建模相当或略弱(例如,给定星系的恒星质量和金属丰度分别为0.17和0.26 dex)。我们还发现,我们的推理框架可以进行快速的SED推理(通过SBI/NPE获得0.9–1.2×105个星系光谱,代价是基于1 MCMC的拟合)。通过这项工作,我们为进一步的工作奠定了基础,重点是在JWST星系调查计划和宽视场罗马太空望远镜光谱调查的时代,用SBI对星系光谱进行SED拟合。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
DIGS: deep inference of galaxy spectra with neural posterior estimation
With the advent of billion-galaxy surveys with complex data, the need of the hour is to efficiently model galaxy spectral energy distributions (SEDs) with robust uncertainty quantification. The combination of simulation-based inference (SBI) and amortized neural posterior estimation (NPE) has been successfully used to analyse simulated and real galaxy photometry both precisely and efficiently. In this work, we utilise this combination and build on existing literature to analyse simulated noisy galaxy spectra. Here, we demonstrate a proof-of-concept study of spectra that is (a) an efficient analysis of galaxy SEDs and inference of galaxy parameters with physically interpretable uncertainties; and (b) amortized calculations of posterior distributions of said galaxy parameters at the modest cost of a few galaxy fits with Markov chain Monte Carlo (MCMC) methods. We utilise the SED generator and inference framework Prospector to generate simulated spectra, and train a dataset of 2 × 106 spectra (corresponding to a five-parameter SED model) with NPE. We show that SBI—with its combination of fast and amortized posterior estimations—is capable of inferring accurate galaxy stellar masses and metallicities. Our uncertainty constraints are comparable to or moderately weaker than traditional inverse-modelling with Bayesian MCMC methods (e.g. 0.17 and 0.26 dex in stellar mass and metallicity for a given galaxy, respectively). We also find that our inference framework conducts rapid SED inference (0.9–1.2 × 105 galaxy spectra via SBI/NPE at the cost of 1 MCMC-based fit). With this work, we set the stage for further work that focuses of SED fitting of galaxy spectra with SBI, in the era of JWST galaxy survey programs and the wide-field Roman Space Telescope spectroscopic surveys.
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来源期刊
Machine Learning Science and Technology
Machine Learning Science and Technology Computer Science-Artificial Intelligence
CiteScore
9.10
自引率
4.40%
发文量
86
审稿时长
5 weeks
期刊介绍: Machine Learning Science and Technology is a multidisciplinary open access journal that bridges the application of machine learning across the sciences with advances in machine learning methods and theory as motivated by physical insights. Specifically, articles must fall into one of the following categories: advance the state of machine learning-driven applications in the sciences or make conceptual, methodological or theoretical advances in machine learning with applications to, inspiration from, or motivated by scientific problems.
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