Computationally Tractable Inference for Multi-Messenger Astrophysics

多信使天体物理学的计算易于处理的推理

• 批准号: 2152746
• 负责人: Galin Jones
• 金额: $ 15万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2152746&HistoricalAwards=false
• 关键词: Computationally; Tractable; Inference; Multi; Messenger

Multi-messenger astrophysics leverages multiple modalities of observations, such as gravitational waves, light, neutrinos, and cosmic rays, to observe astrophysical events and objects. Two specific issues arising in multi-messenger astrophysics motivate much of this research project: detecting cross-correlation between gravitational waves and electromagnetic sky maps and constraining the neutron star equation of state. Addressing these issues formally with the data produced from multi-messenger observations presents challenges that require the development of novel statistical and computational methodology. This project aims to develop improved (1) statistical techniques for detecting cross-correlation between a conjectured gravitational wave background and the cosmic microwave background sky map and (2) Bayesian algorithms for analysis of gravitational wave signatures in binary neutron star mergers. The research will provide interdisciplinary opportunities for professional development of the next generation of statisticians and astronomers. The project will focus on developing methods for performing goodness-of-fit tests for multidimensional parametric models characterized by high computational complexity, paying particular attention to identifying the sources of mismodelling. Another focus will be developing methods for convergence analysis of Markov chain Monte Carlo methods in both low-dimensional (fixed sizes of state space and observed data) and in high-dimensional (size of observed data and state space increase simultaneously) regimes. Convergence analysis in these regimes should be viewed as complementary; without such convergence analyses, practitioners have limited ability to assess the reliability of their MCMC experiments and hence any subsequent inference. The new methods will be made publicly available through open-source software.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.

多理智的天体物理学利用了多种观测方式，例如引力波，光，中微子和宇宙射线，以观察天体物理事件和物体。在多通信天体物理学中引起的两个具体问题激发了该研究项目的大部分：检测引力波和电磁天空图之间的互相关，并限制状态的中子星方程。用来自多理智观察的数据正式解决这些问题，提出了需要开发新型统计和计算方法论的挑战。该项目旨在开发改进的（1）统计技术，用于检测猜想的重力波背景与宇宙微波背景天空图和（2）贝叶斯算法之间的互相关，以分析二元中性恒星合并中的重力波特征。这项研究将为下一代统计学家和天文学家的专业发展提供跨学科的机会。该项目将着重于开发用于执行以高计算复杂性为特征的多维参数模型的拟合优点测试的方法，特别关注识别未种中编码的来源。另一个重点将是开发在低维（状态空间和观察到的数据的固定尺寸）和高维（观察到的数据的大小和状态空间同时增加）方案中的马尔可夫链蒙特卡洛方法收敛分析的方法。这些制度中的收敛分析应被视为互补。没有这样的收敛分析，从业人员可以评估其MCMC实验的可靠性以及随后的推断能力有限。新方法将通过开源软件公开获得。该奖项反映了NSF的法定任务，并认为使用基金会的知识分子优点和更广泛的影响评论标准，被认为值得通过评估。