Collaborative Research: CDS&E: Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) to maximize the science return of next generation cosmological experiments

利用机器学习模拟的宇宙学和天体物理学（camel）项目利用最近在计算星系形成方面的重大进展，生产最大的一套具有完整重子物理的宇宙学模拟，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。这个项目将使用这个独特的数据集来研究如何从下一代宇宙调查中获得最大的科学回报。尽管这些调查将以前所未有的精度约束宇宙学参数的值，但实现这一目标需要克服两个主要障碍：(1)最佳汇总统计是未知的；(2)许多信息在尺度上受到重子过程的显著影响，而这些过程仍然知之甚少。camel将(1)开发神经网络，以帮助提取大多数宇宙信息，(2)在大范围参数上进行数千次模拟，以量化重子效应的不确定性。所有camel数据产品都将公开提供，以使更广泛的社区能够进行研究和参与。该团队将通过为本科生提供专门的指导和早期研究机会，通过三个项目，努力提高女性和代表性不足的少数民族的参与和成功：(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项目；(2) AstroCom NYC项目，与来自纽约城市大学、美国自然历史博物馆和熨斗研究所的其他导师一起；(3)康涅狄格大学的天体物理新项目。即将进行的实验，如DES、DESI、LSST、WFIRST、SKA和欧几里得，将提高我们对基础物理学以及宇宙起源和命运的理解。camel将有助于确定应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，并量化关键天体物理过程（如来自恒星和大质量黑洞的反馈）的子网格模型中的不确定性，这些不确定性限制了流体动力学模拟的使用。camel将使用的神经网络和数千次模拟将比以前的工作产生明显的质量改进。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.