Elements: A new generation of samplers for astronomy and physics

Elements：新一代天文学和物理学采样器

• 批准号: 2311559
• 负责人: Uros Seljak
• 金额: $ 60万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2311559&HistoricalAwards=false
• 关键词: Elements; new; generation; samplers; astronomy

Statistical inference is fundamental to scientific data analysis, and is often facilitated by the Bayesian methodology, which leverages posterior inference via Monte Carlo sampling. Traditionally, this has been a computational challenge, with the methodology often constituting the largest computational expense in many scientific fields, including but not limited to astronomy, cosmology, lattice quantum chromodynamics (QCD), and molecular dynamics. Fortunately, recent years have witnessed considerable academic progress in sampling methods, often resulting in significant computational reductions on synthetic examples. The primary objective of this project is to develop a software infrastructure that facilitates the use of these new and efficient samplers by a diverse range of scientists. The outcome of this research is anticipated to provide speedy and precise samplers that boast a user-friendly interface. These tools are not limited to applications in astronomy and physics but can be applied broadly across various scientific and engineering fields. Additionally, these innovative methodologies can be introduced into the curriculum of courses where statistical inference plays a key role, including courses on Data Science and Statistics for Science, thereby further enhancing the academic and practical understanding of these vital tools in the scientific community.The technical endeavor of this project involves developing software infrastructure for two cutting-edge samplers to facilitate Bayesian uncertainty quantification in various scientific fields. The first sampler, PocoMC, based on the Preconditioned Monte Carlo algorithm, utilizes Normalizing Flows and annealing for swift and accurate posterior analysis. The second sampler, the MicroCanonical Hamiltonian Monte Carlo, employs gradient-based methods effective in very high-dimensional scenarios where gradient-free methods fall short. Both samplers offer significant computational cost reductions compared to existing alternatives, presenting themselves as potential standard tools for scientists applying Bayesian methods. The project's scope encompasses the deployment of these new samplers as standalone packages and their integration into several widely-used Probabilistic Programming Languages, ultimately aiming to revolutionize statistical inference methods used in science and engineering.This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Division of Astronomical Sciences and the Physics at the Information Frontier program in the Division of Physics within the Directorate for Mathematical and Physical Sciences.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.

统计推断是科学数据分析的基础，通常由贝叶斯方法促进，贝叶斯方法通过蒙特卡罗抽样利用后验推断。传统上，这一直是一个计算上的挑战，在许多科学领域，包括但不限于天文学、宇宙学、晶格量子色动力学（QCD）和分子动力学，这种方法通常构成了最大的计算费用。幸运的是，近年来在采样方法方面取得了相当大的学术进展，通常会导致合成示例的显着计算减少。该项目的主要目标是开发一个软件基础设施，以方便各种科学家使用这些新的高效采样器。这项研究的结果预计将提供快速和精确的采样器，拥有一个用户友好的界面。这些工具不仅限于天文学和物理学的应用，而且可以广泛应用于各种科学和工程领域。此外，这些创新的方法可以引入到统计推断发挥关键作用的课程课程中，包括数据科学和科学统计课程，从而进一步增强科学界对这些重要工具的学术和实践理解。该项目的技术努力包括为两个尖端采样器开发软件基础设施，以促进各种科学领域的贝叶斯不确定度量化。第一个采样器PocoMC基于预条件蒙特卡罗算法，利用归一化流和退火进行快速准确的后验分析。第二个采样器是微规范哈密顿蒙特卡罗，它采用基于梯度的方法，在非常高维的情况下有效，而无梯度的方法则无法实现。与现有的替代方案相比，这两种采样器都提供了显著的计算成本降低，使其成为应用贝叶斯方法的科学家的潜在标准工具。该项目的范围包括将这些新的采样器作为独立的软件包进行部署，并将其集成到几种广泛使用的概率编程语言中，最终旨在彻底改变科学和工程中使用的统计推断方法。该奖项由高级网络基础设施办公室颁发，由天文科学部和物理部信息前沿项目的物理部联合支持。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。