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

统计理论和方法在科学发现和进步中发挥着根本作用，包括在现代天文学中，在现代天文学中，收集的数据规模越来越大，种类和复杂性也越来越多。新技术和仪器产生了各种各样的新兴数据类型和数据分析挑战，这反过来又需要和激发更多创新的统计方法和理论。本提案以推进统计基础和前沿的双重目标为指导，以天文学问题为动力，为天文学挑战提供有原则的数据分析解决方案。CHASC国际天文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项目利用了CHASC的记录，在几个新项目中取得了进展。例如，将复杂的天体物理模型与用高科技仪器收集的复杂数据相匹配，通常需要进行一系列的统计分析。几个由英国领导的项目致力于开发新的统计方法，这些方法可以正确地解释错误，并在这些分析序列中推进不确定性。美国领导的额外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。其他由美国主导的项目涉及从二维甚至四维数据中快速自动探测星系等天体。pi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，推动统计前沿的广泛影响，将远远超出天体统计。PIs计划提供有效的方法和算法，以应对天文学中出现的挑战，并希望在天文学研究人员中推广这种有原则的数据分析方法。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发和影响成为可能。

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.