DMS-EPSRC Collaborative Research: Advancing Statistical Foundations and Frontiers from and for Emerging Astronomical Data Challenges

DMS-EPSRC 合作研究：推进统计基础和前沿，应对新出现的天文数据挑战

• 批准号: EP/W015080/1
• 负责人: David Van Dyk
• 金额: $ 34.75万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FW015080%2F1
• 关键词: DMS; EPSRC; Collaborative; Research; Advancing

Statistical theory and methods play a fundamental role in scientific discovery and advancement, including in modern astronomy, where data are collected on increasingly massive scales and with more varieties and complexity. New technology and instrumentation are spawning a diverse array of emerging data types and data analytic challenges, which in turn require and inspire ever more innovative statistical methods and theories. This proposal is guided by the dual aims of advancing statistical foundations and frontiers, motivated by astronomical problems and providing principled data analytic solutions to challenges in astronomy. The CHASC International Center for Astrostatistics has an extensive track record in accomplishing both tasks. This NSF-EPSRC project leverages CHASC's track record to make progress in several new projects. Fitting sophisticated astrophysical models to complex data that were collected with high-tech instruments, for example, often involves a sequence of statistical analyses. Several UK-led projects center on developing new statistical methods that properly account for errors and carry uncertainty forward within such sequences of analyses. Additional US-led work will focus on developing theoretical properties of novel statistical estimation procedures to address data-analytic challenges associated with solar flares and X-ray observations. Other US-led projects involve fast and automatic detection of astronomical objects such as galaxies from 2D or even 4D data. The PIs will develop statistical theory and methods in the context of these projects, building statistical foundations and pushing the frontiers of statistics forward for broad impact that will extend well beyond astrostatistics. The PIs plan to offer effective methods and algorithms for tackling emerging challenges in astronomy, with the aspiration of promoting such principled data-analytic methods among researchers in astronomy. Its provision of free software via the CHASC GitHub Software Library will enable the distribution and impact of the proposed methods and algorithms.

统计理论和方法在科学发现和进步中发挥着重要作用，包括在现代天文学中，收集的数据规模越来越大，种类越来越多，越来越复杂。新的技术和工具正在催生各种各样的新兴数据类型和数据分析挑战，这反过来又需要并激发更多创新的统计方法和理论。这一建议的指导是推进统计基础和前沿的双重目标，由天文学问题和提供原则性的数据分析解决方案，以应对天文学的挑战。CHASC国际天体统计中心在完成这两项任务方面有着广泛的记录。这个NSF-EPSRC项目利用CHASC的跟踪记录在几个新项目中取得进展。例如，将复杂的天体物理模型与高科技仪器收集的复杂数据相匹配，通常涉及一系列统计分析。几个英国主导的项目集中在开发新的统计方法，正确地解释错误，并在这样的分析序列中向前推进不确定性。美国领导的其他工作将侧重于开发新的统计估计程序的理论特性，以解决与太阳耀斑和X射线观测相关的数据分析挑战。其他由美国领导的项目涉及从2D甚至4D数据中快速自动检测天文物体，如星系。研究所将在这些项目的背景下发展统计理论和方法，建立统计基础，推动统计前沿，产生远远超出天体统计的广泛影响。PI计划提供有效的方法和算法来应对天文学中的新挑战，并希望在天文学研究人员中推广这种原则性的数据分析方法。它通过CHASC GitHub软件库提供免费软件，将使所提出的方法和算法的分发和影响成为可能。

项目成果

Stratified Learning: a general-purpose statistical method for improved learning under Covariate Shift

• DOI: 10.1002/sam.11643
• 发表时间: 2021-06
• 期刊: Statistical Analysis and Data Mining: The ASA Data Science Journal
• 作者: Maximilian Autenrieth;D. V. Dyk;R. Trotta;D. Stenning

TD-CARMA: Painless, Accurate, and Scalable Estimates of Gravitational Lens Time Delays with Flexible CARMA Processes

TD-CARMA：通过灵活的 CARMA 流程对引力透镜时间延迟进行无痛、准确且可扩展的估计

• DOI: 10.3847/1538-4357/acbea1
• 发表时间: 2023
• 期刊: The Astrophysical Journal
• 作者: Meyer A

Identifying Diffuse Spatial Structures in High-energy Photon Lists

识别高能光子列表中的漫射空间结构

• DOI: 10.3847/1538-3881/aca478
• 发表时间: 2023
• 期刊: The Astronomical Journal
• 作者: Fan, Minjie;Wang, Jue;Kashyap, Vinay L.;Lee, Thomas C. M.;van Dyk, David A.;Zezas, Andreas
• 通讯作者: Zezas, Andreas