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

DMS-EPSRC 合作研究：为新出现的天文数据挑战推进统计基础和前沿

• 批准号: 2113397
• 负责人: Yang Chen
• 金额: $ 16万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2113397&HistoricalAwards=false
• 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 research 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 (California-Harvard Astrostatistics Collaboration) International Center has an extensive track record in accomplishing both tasks. This research 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 projects center on developing new statistical methods that properly account for errors and carry uncertainty forward within such sequences of analyses. Additional 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 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. The projects reflect three broad themes: (1) Exploring fundamental statistical theory with immediate impact in astronomy, including a general approach for obtaining confidence regions by leveraging the pivot-property of maximal product spacing, which is then applied to assess the power law of solar flares, and a statistically principled correction to the use of the popular C-stat in astrophysics; (2) Assessing the misspecification of models and prior distributions in multi-stage statistical analyses, and post processing posterior draws to correct for defects in prior modeling when redoing a Bayesian analysis is impractical; and (3) Identifying breakpoints in complex models, which includes a fast algorithm for identifying astronomical boundaries and identifying breakpoints in joint spatial, spectral, temporal models. Theme 1 is more theory driven, while Themes 2 and 3 are more methods and computation driven. Together they form a rich suite of case studies for developing statistical methods for astronomical problems, ranging from new theoretical foundations to innovative modeling strategies and to efficient computational techniques. Consequently, the research will impact both the fields of statistics and astronomy: spurring more interest and new problems for statisticians and resolving long standing problems in astronomy.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，并且具有更大的变化和复杂性。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，这反过来又需要并启发了更多创新的统计方法和理论。这项研究的指导是推进统计基础和前沿的双重目的，该基础和前沿，这是由天文学问题和为天文学挑战提供主要数据分析解决方案的动机。 CHASC（加利福尼亚州的Astrostatistics合作）国际中心在完成这两项任务方面都有广泛的记录。这项研究利用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。几个项目集中在开发新的统计方法上，这些方法可以正确解释错误，并在此类分析序列内将不确定性转发。其他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中的新兴挑战，并渴望在天文学研究人员中促进这种主要的数据分析方法。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。这些项目反映了三个广泛的主题：（1）探索基本统计理论，对天文学有直接影响，包括通过利用最大产品间距的枢轴 - 份量来获得置信区的一般方法，然后将其应用于太阳能弹性的权力法，以及一种统计上的主要校正，以对流行的C-Stat in Altrophyss In in Altophyssics In Attrophyssics进行使用； （2）在多阶段统计分析中评估模型和先前分布的错觉，并在重复贝叶斯分析时在先前建模时纠正后期处理后的处理后绘制不切实际； （3）识别复杂模型中的断点，其中包括一种快速算法，用于识别天文界限并识别关节空间，光谱，临时模型中的断点。主题1是更多理论驱动的，而主题2和3是更多的方法和计算驱动器。它们共同构成了一系列案例研究，用于为天文问题开发统计方法，从新的理论基础到创新建模策略到有效的计算技术。因此，这项研究将影响统计和天文学领域：激发统计学家的更多兴趣和新问题，并解决天文学中的长期存在问题。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响来评估来获得的支持。

项目成果

New Findings From Explainable SYM‐H Forecasting Using Gradient Boosting Machines

• DOI: 10.1029/2021sw002928
• 发表时间: 2022-07
• 期刊: Space Weather
• 作者: Daniel Iong;Yang Chen;G. Tóth;S. Zou;Tuija Pulkkinen;Jiaen Ren;E. Camporeale;T. Gombosi

Improved and Interpretable Solar Flare Predictions With Spatial and Topological Features of the Polarity Inversion Line Masked Magnetograms

• DOI: 10.1029/2021sw002837
• 发表时间: 2021-11
• 期刊: Space Weather
• 作者: Hu Sun;W. Manchester;Yang Chen

Concordance: In-flight Calibration of X-Ray Telescopes without Absolute References

一致性：没有绝对参考的 X 射线望远镜的飞行中校准

• DOI: 10.3847/1538-3881/ac230a
• 发表时间: 2021
• 期刊: The Astronomical Journal
• 作者: Marshall, Herman L.;Chen, Yang;Drake, Jeremy J.;Guainazzi, Matteo;Kashyap, Vinay L.;Meng, Xiao-Li;Plucinsky, Paul P.;Ratzlaff, Peter;van Dyk, David A.;Wang, Xufei
• 通讯作者: Wang, Xufei

MATRIX COMPLETION METHODS FOR THE TOTAL ELECTRON CONTENT VIDEO RECONSTRUCTION

• DOI: 10.1214/21-aoas1541
• 发表时间: 2022-09-01
• 期刊: ANNALS OF APPLIED STATISTICS
• 影响因子: 1.8
• 作者: Sun, Hu;Hua, Zhijun;Chen, Yang
• 通讯作者: Chen, Yang