Collaborative Research: CDS&E: Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) to Maximize the Science Return of next-generation cosmological experiments

合作研究：CDS

• 批准号: 2108078
• 负责人: Francisco Villaescusa-Navarro
• 金额: $ 28.06万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2108078&HistoricalAwards=false
• 关键词: Collaborative; Research; CDS& Cosmology; Astrophysics

The Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project leverages recent major advances in computational galaxy formation to produce the largest suite of cosmological simulations with full baryonic physics designed to train machine learning algorithms for a broad range of applications, including thousands of cosmological and feedback parameter variations. This project will use this unique dataset to study how to maximize the science return from next generation cosmological surveys. Although the surveys will constrain the value of the cosmological parameters with unprecedented accuracy, achieving this goal requires overcoming two major obstacles: (1) the optimal summary statistic is unknown, and (2) a lot of the information is on scales significantly affected by baryonic processes that are still poorly understood. CAMELS will (1) develop neural networks to help extract the most cosmological information, and (2) perform thousands of simulations over a wide range of parameters to quantify uncertainties in baryonic effects. All CAMELS data products will be publicly available, to enable research and engagement by the broader community. The team will work to increase the participation and success of women and underrepresented minorities by providing dedicated mentoring and early access to research, through three programs for undergraduate students: (1) a summer research program co-organized by the National Society of Black Physicists and the Simons Observatory; (2) the AstroCom NYC program, joining other mentors from the City University of New York, the American Museum of Natural History, and the Flatiron Institute; and (3) the new Colors of Astrophysics program at the University of Connecticut.Upcoming experiments such as DES, DESI, LSST, WFIRST, SKA, and Euclid will improve our understanding of fundamental physics and the origin and fate of the Universe. CAMELS will help to determine the optimal summary statistic to apply to the non-Gaussian density fields observed in most cosmological surveys, and to quantify uncertainties in subgrid models for key astrophysical processes such as feedback from stars and massive black holes, which limit the use of hydrodynamic simulations. The neural networks and thousands of simulations that will be used by CAMELS will produce a distinct qualitative improvement over previous work.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

宇宙学和天体物理学与机器学习模拟（CAMELS）项目利用计算星系形成的最新重大进展，产生最大的宇宙学模拟套件，具有完整的重子物理学，旨在为广泛的应用训练机器学习算法，包括数千个宇宙学和反馈参数变化。 该项目将使用这个独特的数据集来研究如何最大限度地提高下一代宇宙学调查的科学回报。 尽管这些调查将以前所未有的准确性限制宇宙学参数的值，但实现这一目标需要克服两个主要障碍：（1）最佳汇总统计量未知，以及（2）许多信息是在受重子过程显着影响的尺度上，而这些过程仍然知之甚少。 CAMELS将（1）开发神经网络以帮助提取大多数宇宙学信息，（2）在广泛的参数范围内进行数千次模拟，以量化重子效应的不确定性。 CAMELS的所有数据产品都将公开提供，以便更广泛的社区进行研究和参与。 该小组将通过为本科生提供三个方案，努力提高妇女和代表性不足的少数民族的参与和成功，提供专门的指导和抢先体验研究：（1）由全国黑人物理学家协会和西蒙斯天文台共同组织的夏季研究方案;（2）AstroCom NYC计划，加入来自纽约城市大学、美国自然历史博物馆和熨斗研究所的其他导师;（3）康涅狄格大学新的天体物理学色彩计划。即将进行的DES、DESI、LSST、WFIRST、SKA和欧几里得等实验将提高我们对基础物理和宇宙起源与命运的理解。 CAMELS将有助于确定适用于大多数宇宙学调查中观察到的非高斯密度场的最佳汇总统计量，并量化关键天体物理过程的次网格模型中的不确定性，例如来自恒星和大质量黑洞的反馈，这限制了流体动力学模拟的使用。 CAMELS将使用的神经网络和数千个模拟将产生比以前的工作明显的质量改进。该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

Studying the warm hot intergalactic medium in emission: a reprise

研究发射中的暖热星际介质：重演

• DOI: 10.1093/mnras/stad1495
• 发表时间: 2023
• 期刊: Monthly Notices of the Royal Astronomical Society
• 影响因子: 4.8
• 作者: Parimbelli, G.;Branchini, E.;Viel, M.;Villaescusa-Navarro, F.;ZuHone, J.
• 通讯作者: ZuHone, J.

Robust Field-level Inference of Cosmological Parameters with Dark Matter Halos

暗物质晕宇宙学参数的鲁棒场级推断

• DOI: 10.3847/1538-4357/acac7a
• 发表时间: 2023
• 期刊: The Astrophysical Journal
• 作者: Shao, Helen;Villaescusa-Navarro, Francisco;Villanueva-Domingo, Pablo;Teyssier, Romain;Garrison, Lehman H.;Gatti, Marco;Inman, Derek;Ni, Yueying;Steinwandel, Ulrich P.;Kulkarni, Mihir
• 通讯作者: Kulkarni, Mihir

Robust Field-level Likelihood-free Inference with Galaxies

稳健的星系场级无似然推理

• DOI: 10.3847/1538-4357/acd1e2
• 发表时间: 2023
• 期刊: The Astrophysical Journal
• 作者: de Santi, Natalí S.;Shao, Helen;Villaescusa-Navarro, Francisco;Abramo, L. Raul;Teyssier, Romain;Villanueva-Domingo, Pablo;Ni, Yueying;Anglés-Alcázar, Daniel;Genel, Shy;Hernández-Martínez, Elena
• 通讯作者: Hernández-Martínez, Elena