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首页> 外文期刊>The Astrophysical Journal. Supplement Series >A Comprehensive Bayesian Discrimination of the Simple Stellar Population Model, Star Formation History, and Dust Attenuation Law in the Spectral Energy Distribution Modeling of Galaxies
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A Comprehensive Bayesian Discrimination of the Simple Stellar Population Model, Star Formation History, and Dust Attenuation Law in the Spectral Energy Distribution Modeling of Galaxies

机译：全面的贝叶斯歧视简单恒星人口模型，星形地层历史和粉尘衰减法在星系的光谱能量分布建模中

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摘要

When modeling and interpreting the spectral energy distributions (SEDs) of galaxies, the simple stellar population (SSP) model, star formation history (SFH), and dust attenuation law (DAL) are three of the most important components. However, each of them carries significant uncertainties that have seriously limited our ability to reliably recover the physical properties of galaxies from the analysis of their SEDs. In this paper, we present a Bayesian framework to deal with these uncertain components simultaneously. Based on the Bayesian evidence, a quantitative implement of the principle of Occam's razor, the method allows a more objective and quantitative discrimination among the different assumptions about these uncertain components. With a K-s-selected sample of 5467 low-redshift (mostly with z less than or similar to 1) galaxies in the COSMOS/UltraVISTA field and classified into passively evolving galaxies (PEGs) and star-forming galaxies (SFGs) with the UVJ diagram, we present a Bayesian discrimination of a set of 16 SSP models from five research groups (BC03 and CB07, M05, GALEV, Yunnan-II, BPASS. V2.0), five forms of SFH (Burst, Constant, Exp-dec, Exp-inc, Delayed-tau), and four kinds of DAL (Calzetti law, MW, LMC, SMC). We show that the results obtained with the method are either obvious or understandable in the context of stellar/galaxy physics. We conclude that the Bayesian model comparison method, especially that for a sample of galaxies, is very useful for discriminating the different assumptions in the SED modeling of galaxies.
机译：在建模和解释星系的光谱能量分布（SED）时，简单的恒星群体（SSP）模型，星形成历史（SFH）和灰尘衰减法（DAL）是最重要的三个组件。然而，它们中的每一个都带有重大的不确定性，严重限制了我们可靠地从他们的SED分析中可靠地恢复星系的物理性质。在本文中，我们展示了一个贝叶斯框架，同时处理这些不确定的组件。基于贝叶斯证据的基础上，该方法的定量实施了欧洲侵略者的原则，该方法允许对这些不确定组成部分的不同假设进行更客观和定量的歧视。 ks-selected样本为5467低频（主要是z小于或类似于1）星系，在宇宙/ ultravista字段中，并分为与UVJ图的被动发展的星系（钉）和星形星系（SFG） ，我们展示了来自五个研究组的一组16个SSP模型的贝叶斯歧视（BC03和CB07，M05，Galev，Yunnan-II，Bpass。V2.0），SFH的五种形式（突发，常数，Exp-Dec， Exp-Inc，Delayed-Tau）和四种Dal（Calzetti Law，MW，LMC，SMC）。我们表明，在恒星/星系物理学的背景下，用该方法获得的结果是显而易见的或可以理解的。我们得出结论，贝叶斯模型的比较方法，特别是对于星系的样本，非常有用，可用于区分星系的SED建模中的不同假设。

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