利用机器学习和星系成团性约束宇宙学：CAMELS-SAM套件
Constraining cosmology with machine learning and galaxy clustering: the CAMELS-SAM suite
AI总结：
本文开发了CAMELS-SAM星系模拟套件，利用神经网络结合星系成团性统计量，在边缘化天体物理参数后，将宇宙学参数约束至3-8%误差，并揭示了其对反馈参数的约束力。
摘要：
随着下一代大型星系巡天上线运行，开发并理解用于分析海量天文数据的机器学习工具正变得日益重要。神经网络功能强大，能够探测数据中的深层模式，但必须在大型且具代表性的数据集上进行仔细训练。我们开发并生成了机器学习模拟宇宙学与天体物理学（CAMELS）项目的一个新“驼峰”：CAMELS-SAM，它包含一千个体积为(100 $h^{-1}$ cMpc)$^3$的纯暗物质模拟，具有不同的宇宙学参数（$Ω_m$和$σ_8$），并在广泛的天体物理参数范围内通过Santa Cruz半解析模型运行星系形成。作为对这一大体积、宽参数空间下海量模拟星系套件能力的概念验证，我们探究了简单成团性汇总统计在边缘化天体物理学效应并利用神经网络约束宇宙学方面的能力。我们使用了两点相关函数、单元格计数以及空洞概率函数，并探测了$0.68<$ R $<27\ h^{-1}$ cMpc范围内的非线性和线性尺度。我们的宇宙学约束集中在$Ω_{\text{M}}$和$σ_8$上3-8$\\%$的误差范围内，并探讨了各种星系选择、星系采样以及成团性统计量选择对这些约束的影响。我们还额外探讨了这些成团性统计量如何约束并告知Santa Cruz SAM中关键的恒星与星系反馈参数。CAMELS-SAM已与CAMELS的其余部分一同公开发布，为天体物理学中机器学习的诸多应用提供了巨大潜力：https://camels-sam.readthedocs.io。
英文摘要：
As the next generation of large galaxy surveys come online, it is becoming increasingly important to develop and understand the machine learning tools that analyze big astronomical data. Neural networks are powerful and capable of probing deep patterns in data, but must be trained carefully on large and representative data sets. We developed and generated a new `hump' of the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project: CAMELS-SAM, encompassing one thousand dark-matter only simulations of (100 $h^{-1}$ cMpc)$^3$ with different cosmological parameters ($Ω_m$ and $σ_8$) and run through the Santa Cruz semi-analytic model for galaxy formation over a broad range of astrophysical parameters. As a proof-of-concept for the power of this vast suite of simulated galaxies in a large volume and broad parameter space, we probe the power of simple clustering summary statistics to marginalize over astrophysics and constrain cosmology using neural networks. We use the two-point correlation function, count-in-cells, and the Void Probability Function, and probe non-linear and linear scales across $0.68<$ R $<27\ h^{-1}$ cMpc. Our cosmological constraints cluster around 3-8$\%$ error on $Ω_{\text{M}}$ and $σ_8$, and we explore the effect of various galaxy selections, galaxy sampling, and choice of clustering statistics on these constraints. We additionally explore how these clustering statistics constrain and inform key stellar and galactic feedback parameters in the Santa Cruz SAM. CAMELS-SAM has been publicly released alongside the rest of CAMELS, and offers great potential to many applications of machine learning in astrophysics: https://camels-sam.readthedocs.io.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
