Cosmological back-reaction of baryons on dark matter in the CAMELS simulations
University of Connecticut（康涅狄格大学）；Flatiron Institute（平顿研究所）；Columbia University（哥伦比亚大学）；Yale University（耶鲁大学）；University of Cambridge（剑桥大学）
Matthew Gebhardt, Daniel Anglés-Alcázar, Shy Genel, Daisuke Nagai, Boon Kiat Oh, Isabel Medlock, Jonathan Mercedes-Feliz, Sagan Sutherland, Max E. Lee, Xavier S… 展开作者
Matthew Gebhardt, Daniel Anglés-Alcázar, Shy Genel, Daisuke Nagai, Boon Kiat Oh, Isabel Medlock, Jonathan Mercedes-Feliz, Sagan Sutherland, Max E. Lee, Xavier Sims, Christopher C. Lovell, David N. Spergel, Romeel Davé, Matthieu Schaller, Joop Schaye, Francisco Villaescusa-Navarro
Baryonic processes such as radiative cooling and feedback from massive stars and active galactic nuclei (AGN) directly redistribute baryons in the Universe but also indirectly redistribute dark matter due to changes in the gravitational potential. In this work, we investigate this "back-reaction" of baryons on dark matter using thousands of cosmological hydrodynamic simulations from the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project, including parameter variations in the SIMBA, IllustrisTNG, ASTRID, and Swift-EAGLE galaxy formation models. Matching haloes to corresponding N-body (dark matter-only) simulations, we find that virial masses decrease owing to the ejection of baryons by feedback. Relative to N-body simulations, halo profiles show an increased dark matter density in the center (due to radiative cooling) and a decrease in density farther out (due to feedback), with both effects being strongest in SIMBA (> 450% increase at r < 0.01 Rvir). The clustering of dark matter strongly responds to changes in baryonic physics, with dark matter power spectra in some simulations from each model showing as much as 20% suppression or increase in power at k ~ 10 h/Mpc relative to N-body simulations. We find that the dark matter back-reaction depends intrinsically on cosmology (Omega_m and sigma_8) at fixed baryonic physics, and varies strongly with the details of the feedback implementation. These results emphasize the need for marginalizing over uncertainties in baryonic physics to extract cosmological information from weak lensing surveys as well as their potential to constrain feedback models in galaxy evolution.
The CAMELS project: Expanding the galaxy formation model space with new ASTRID and 28-parameter TNG and SIMBA suites
Harvard-Smithsonian Center for Astrophysics（哈佛-史密松天体物理中心）；Carnegie Mellon University（卡内基梅隆大学）；Flatiron Institute（弗莱提荣研究所）；Columbia University（哥伦比亚大学）；University of Connecticut（康涅狄格大学）；Princeton University（普林斯顿大学）；University of California, Riverside（加州大学河滨分校）
The CAMELS-CROCODILE Simulation Suite: A New Cosmology--Astrophysics Playground for Machine Learning
The University of Osaka（大阪大学）；The University of Tokyo（东京大学）；University of Nevada, Las Vegas（内华达大学拉斯维加斯分校）；Nevada Center for Astrophysics（内华达天体物理中心）；Nagoya University（名古屋大学）；Princeton University（普林斯顿大学）；Flatiron Institute（平顿研究所）
本文介绍 CAMELS-CROCODILE 模拟套件，扩展 CAMELS 框架以独立实现重子物理，并验证其与 IllustrisTNG 的差异，同时展示图神经网络跨模型推断的局限性，强调跨物理模型训练和测试 ML 的必要性。
Learning the Universe with the 2nd Generation of CAMELS: Varying 35 parameters of the IllustrisTNG model in (50Mpc/h)^3 boxes
Center for Computational Astrophysics, Flatiron Institute（平顿研究所计算天体物理学中心）；Columbia Astrophysics Laboratory, Columbia University（哥伦比亚大学天体物理实验室，哥伦比亚大学）；NSF-Simons AI Institute for the Sky (SkAI)（NSF-西蒙斯天空人工智能研究所（SkAI））；School of Physics, Korea Institute for Advanced Study（韩国高等科学研究院物理学院）；Department of Physics, University of Connecticut（康涅狄格大学物理系）；Department of Physics and Astronomy, Rutgers University（罗格斯大学物理与天文系）；Department of Astronomy, Columbia University（哥伦比亚大学天文系）；Department of Astrophysical Sciences, Princeton University（普林斯顿大学天体物理科学系）；TransferLab, appliedAI Institute for Europe（欧洲应用人工智能研究所 TransferLab）；Kavli Institute for Cosmology, Madingley Road, Cambridge CB3 0HA, UK（剑桥大学卡弗里宇宙学研究所）；Institute of Astronomy, Madingley Road, Cambridge CB3 0HA, UK（剑桥大学天文学研究所）
Baryonic feedback suppression of the matter power spectrum: a three-parameter fitting formula and its single-parameter reduction
School of Physics and Astronomy, Shanghai Jiao Tong University（上海交通大学物理与天文学院）；Tsung-Dao Lee Institute, Shanghai Jiao Tong University（上海交通大学汤耀道研究所）；State Key Laboratory of Dark Matter Physics（暗物质物理国家重点实验室）；Key Laboratory for Particle Astrophysics and Cosmology (MOE)/Shanghai Key Laboratory for Particle Physics and Cosmology（粒子天体物理与宇宙学教育部重点实验室/上海粒子物理与宇宙学重点实验室）
Simulation-based inference from the Lyman-alpha forest 1D power spectrum with CAMELS
Institute for Fundamental Physics of the Universe (IFPU)（宇宙基础物理研究所）；SISSA - International School for Advanced Studies（国际高等研究院）；INAF - Osservatorio Astronomico di Trieste（意大利国家天体物理研究所的里雅斯特天文台）；INFN – National Institute for Nuclear Physics（意大利国家核物理研究所）；Instituto de Astrofísica de Canarias（加那利群岛天体物理学研究所）；Departamento de Astrofísica, Universidad de La Laguna（拉帕尔马大学天体物理学系）；ICSC - Centro Nazionale di Ricerca in High Performance Computing, Big Data e Quantum Computing（高性能计算、大数据与量子计算国家研究中心）
syren-baryon: Analytic emulators for the impact of baryons on the matter power spectrum
Evolutionary Algorithms Laboratory, University of Applied Sciences Upper Austria, Hagenberg, Austria Astrophysics, University of Oxford, Denys Wilkinson Building, Keble Road, Oxford OX1 3RH, UK Institute of Cosmology \& Gravitation, University of Portsmouth, Dennis Sciama Building, Portsmouth, PO1 3FX, UK
Baryonic matter perturbations in decaying vacuum cosmology
我们考虑宇宙重子流体的微扰动力学，并确定了$Λ(t)$ CDM模型的相应功率谱，在该模型中，宇宙学项随哈勃速率线性衰减为暗物质。该模型进行了测试，从Ia型超新星（SNIa）（宪法和联盟2.1），重子声振荡（BAO），各向异性谱的宇宙微波背景（CMB）和大尺度结构（LSS）数据（SDSS DR 7）的第一个峰值的位置的数据的联合分析。虽然均匀和各向同性的背景动力学是由重子只有轻微的影响，有微扰水平上的修改，如果一个单独的保守重子流体包括在内。考虑到当前的重子分数作为一个自由参数，我们重现了观测到的$5\%$的数量级的丰度独立的暗物质的数量级为$32\%$的这个模型。一般来说，背景和微扰动力学之间的一致性得到改善，如果明确考虑重子。
The SZ flux-mass ($Y$-$M$) relation at low halo masses: improvements with symbolic regression and strong constraints on baryonic feedback
Institute for Advanced Study（高等研究院）；Princeton University（普林斯顿大学）；Columbia University（哥伦比亚大学）；Flatiron Institute（熨斗研究院）；University of Pennsylvania（宾夕法尼亚大学）；Yale University（耶鲁大学）
