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起着基本作用，包括在现代天文学中，在现代天文学中，在越来越大的尺度上收集了数据，并具有更多的品种和复杂性。新的技术和仪器正在产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了更具创新性的统计方法和理论。该提案的指导是推进统计基础和前沿的双重目的，该目标是由天文学问题和为天文学挑战提供原则性的数据分析解决方案的动机。 CHASC国际天线列表中心在完成这两个任务方面都有广泛的记录。这个NSF-EPSRC项目利用Chasc的往绩在几个新项目中取得了进步。例如，将复杂的天体物理模型拟合到使用高科技仪器收集的复杂数据中，通常涉及一系列统计分析。英国领导的几个项目中心开发新的统计方法，这些方法适当说明了错误，并在此类分析序列内将不确定性转发。额外的美国领导的工作将着重于开发新型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。其他美国领导的项目涉及快速自动检测天文对象，例如来自2D甚至4D数据的星系。 PI将在这些项目的背景下开发统计理论和方法，建立统计基础，并将统计的前沿前进产生广泛的影响，这将远远超出天文界。 PIS计划提供有效的方法和算法来应对天文学的新兴挑战，并渴望在天文学研究人员中促进这种原则性的数据分析方法。它通过Chasc GitHub软件库提供的免费软件将使所提出的方法和算法的分布和影响能够。

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

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

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