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

带机器学习的宇宙学和天体物理学模拟(CAMEL)项目利用计算星系形成方面的最新重大进展，利用全重子物理产生最大的一套宇宙学模拟，旨在为广泛的应用训练机器学习算法，包括数千种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精确度限制宇宙学参数的值，但要实现这一目标需要克服两个主要障碍：(1)最佳汇总统计数据未知；(2)许多信息是在受到重子过程显著影响的尺度上获得的，而重子过程仍然知之甚少。骆驼将(1)开发神经网络来帮助提取最多的宇宙学信息，以及(2)在广泛的参数范围内进行数千次模拟，以量化重子效应中的不确定性。所有骆驼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及早接触研究的三个项目，努力增加女性和代表不足的少数族裔的参与和成功，这三个项目是：(1)由全国黑人物理学家协会和西蒙斯天文台联合组织的暑期研究项目；(2)AstroCom NYC项目，与纽约城市大学、美国自然历史博物馆和熨斗研究所的其他导师一起；以及(3)康涅狄格大学天体物理学的新颜色计划。即将推出的DES、DESI、LSST、WFIRST、SKA和欧几里德等实验将提高我们对基础物理和宇宙起源和命运的理解。CAMELS将帮助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数据，并量化关键天体物理过程的亚格子模型中的不确定性，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。骆驼将使用的神经网络和数千个模拟将产生明显的质量改进，与以前的工作相比。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.