CDS&E: Probabilistic modeling of fields and point clouds in cosmology

宇宙学观测为了解基础物理提供了一个独特的窗口，例如原始宇宙的超高能物理。即将到来的星系调查，如鲁宾天文台，将产生大量数据，但从这些数据中提取基础物理将是困难的。为了充分利用即将到来的实验的统计敏感性，必须开发利用高性能计算和机器学习的新方法。在这个项目中，来自威斯康星大学麦迪逊分校的科学家将利用概率机器学习的新技术，并将它们应用于宇宙的暗物质和星系分布。使用这些工具，该团队将能够比以前更精确地从星系数据中测量基本物理参数。该研究项目还将为威斯康星大学麦迪逊分校URS项目的几名本科生提供令人兴奋的研究机会，该项目支持和鼓励具有非传统背景的学生，为推广努力做出贡献，并改善科学人工智能重要领域的教育。该项目的科学目标是开发归一化流动来模拟两种类型的宇宙学数据及其统计联系：场级数据，如非线性物质场，以及点云数据，如晕和星系。对于这项任务，该团队将适应最近开发的点云归一化流动。类似的任务也出现在三维计算机视觉和分子设计中，但宇宙学具有独特的特性，这些特性将用于新的机器学习开发。该团队将设计一种尺度分解来处理非常大的点云，然后将它们的归一化流动用于宇宙学中的两个重要应用。第一个应用将是产生超分辨率模拟，其中条件归一化流被用来增加暗物质模拟的分辨率，以及包括重子物理。第二个应用将是利用点云流在正向模拟框架中建立暗物质到星系的概率连接，这将改善与以前方法相比的宇宙初始条件的重建。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。

Cosmological observations provide a unique window into fundamental physics, such as the ultra-high energy physics of the primordial universe. Upcoming galaxy surveys such as Rubin Observatory will produce vast amounts of data, but the extraction of fundamental physics from this data will be difficult. To fully exploit the statistical sensitivity of upcoming experiments, new methods which leverage high performance computing and machine learning must be developed. In this project scientists from the University of Wisconsin, Madison will make use of novel techniques from probabilistic machine learning and apply them to the dark matter and galaxy distribution of the universe. Using these tools, the team will be able to measure fundamental physics parameters from galaxy data more precisely than was previously possible. This research project will also provide exciting research opportunities for several undergraduate students from UW Madison's URS program, which supports and encourages students with non-traditional backgrounds, contribute to outreach efforts, and improve education in the important field of artificial intelligence for science. The scientific goal of this project is to develop normalizing flows to model two types of cosmological data, as well as their statistical connection: field level data, such as the non-linear matter field, and point cloud data such as halos and galaxies. For this task, the team will adapt recently developed normalizing flows for point clouds. Similar tasks appear in 3-dimensional computer vision and molecular design, but cosmology has unique properties that will feed into new machine learning developments. The team will design a scale decomposition to treat very large point clouds and will then use their normalizing flows for two important applications in cosmology. The first application will be to generate super-resolution simulations, where a conditional normalizing flow is used to augment the resolution of dark matter simulations, as well as to include baryonic physics. The second application will be to use the point cloud flow to establish a probabilistic dark matter to galaxy connection in a forward modeling framework, which will improve the reconstruction of cosmological initial conditions with respect to previous approaches.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.