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用机器学习增强天体物理标度关系：应用于减少Sunyaev-Zeldovich通量质量散射

Augmenting astrophysical scaling relations with machine learning: application to reducing the Sunyaev-Zeldovich flux-mass scatter

Institute for Advanced Study（普林斯顿高等研究院）；New York University（纽约大学）；Princeton University（普林斯顿大学）；Flatiron Institute（熨斗研究所）；Columbia University（哥伦比亚大学）；Cornell University（康奈尔大学）
Digvijay Wadekar, Leander Thiele, Francisco Villaescusa-Navarro, J. Colin Hill, Miles Cranmer, David N. Spergel, Nicholas Battaglia, Daniel Anglés-Alcázar, Lars… 展开作者

Digvijay Wadekar, Leander Thiele, Francisco Villaescusa-Navarro, J. Colin Hill, Miles Cranmer, David N. Spergel, Nicholas Battaglia, Daniel Anglés-Alcázar, Lars Hernquist, Shirley Ho

备注：
Version appearing in PNAS. Added additional tests but results unchanged compared to previous version. The code and data associated with this paper are available at https://github.com/JayWadekar/ScalingRelations_ML
发表信息：
PNAS March 17, 2023, 120 (12) e2202074120
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摘要：

复杂的天体物理系统通常表现出可观测属性之间的低散射关系（例如，光度、速度色散、振荡周期）。这些比例关系阐明了基本的物理学，并可以为估计质量和距离提供观测工具。机器学习可以提供一种快速而系统的方法来搜索抽象高维参数空间中的新尺度关系（或对现有关系的简单扩展）。我们使用一种名为符号回归（SR）的机器学习工具，它以分析方程的形式对数据集中的模式进行建模。我们专注于Sunyaev-Zeldovich通量$-$集群质量关系（$Y_\mathrm{SZ}-M$），其中的分散影响推断的宇宙学参数从集群丰度数据。利用来自IllustrisTNG流体动力学模拟的数据上的SR，我们发现了一个新的代理团簇质量结合$Y_\mathrm{SZ}$和电离气体浓度（$c_\mathrm{gas}$）：$M \propto Y_\mathrm{conc}^{3/5} \equiv Y_\mathrm{SZ}^{3/5} (1-A\, c_\mathrm{gas})$。与仅使用$Y_\mathrm{SZ}$相比，$Y_\mathrm{conc}$通过$\sim 20-30$ \%减少了大簇（$M\gtrsim 10^{14}\, h^{-1} \, M_\odot$）的预测$M$中的分散。我们发现，依赖于$c_\mathrm{gas}$的集群表现出更大的分散比他们的郊区的核心。最后，我们测试$Y_\mathrm{conc}$从CAMELS模拟集群，并表明，$Y_\mathrm{conc}$是强大的宇宙学，次网格物理和宇宙方差的变化。我们的研究结果和方法可以用于准确的多波长集群质量估计从即将到来的CMB和X射线调查，如ACT，SO，eROSITA和CMB-S4。

英文摘要：

Complex astrophysical systems often exhibit low-scatter relations between observable properties (e.g., luminosity, velocity dispersion, oscillation period). These scaling relations illuminate the underlying physics, and can provide observational tools for estimating masses and distances. Machine learning can provide a fast and systematic way to search for new scaling relations (or for simple extensions to existing relations) in abstract high-dimensional parameter spaces. We use a machine learning tool called symbolic regression (SR), which models patterns in a dataset in the form of analytic equations. We focus on the Sunyaev-Zeldovich flux$-$cluster mass relation ($Y_\mathrm{SZ}-M$), the scatter in which affects inference of cosmological parameters from cluster abundance data. Using SR on the data from the IllustrisTNG hydrodynamical simulation, we find a new proxy for cluster mass which combines $Y_\mathrm{SZ}$ and concentration of ionized gas ($c_\mathrm{gas}$): $M \propto Y_\mathrm{conc}^{3/5} \equiv Y_\mathrm{SZ}^{3/5} (1-A\, c_\mathrm{gas})$. $Y_\mathrm{conc}$ reduces the scatter in the predicted $M$ by $\sim 20-30$\% for large clusters ($M\gtrsim 10^{14}\, h^{-1} \, M_\odot$), as compared to using just $Y_\mathrm{SZ}$. We show that the dependence on $c_\mathrm{gas}$ is linked to cores of clusters exhibiting larger scatter than their outskirts. Finally, we test $Y_\mathrm{conc}$ on clusters from CAMELS simulations and show that $Y_\mathrm{conc}$ is robust against variations in cosmology, subgrid physics, and cosmic variance. Our results and methodology can be useful for accurate multiwavelength cluster mass estimation from upcoming CMB and X-ray surveys like ACT, SO, eROSITA and CMB-S4.

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