Elements: Scalable Bayesian Software for Interpreting Astronomical Images

Elements：用于解释天文图像的可扩展贝叶斯软件

基本信息

批准号：
2209720
负责人：
Jeffrey Regier
金额：
$ 50万
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：
2022
资助国家：
美国
起止时间：
2022-09-01 至 2025-08-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2209720&HistoricalAwards=false
关键词：
Elements Scalable Bayesian Software Interpreting

项目摘要

The BLISS (Bayesian Light Source Separator) Project is an interdisciplinary research effort to develop a software tool that allows astronomers to make use of the latest advances in machine learning. By harnessing these advances, astronomers can rapidly analyze vast quantities of complex data to understand the nature of our universe. This project also engages and educates a wider audience through a workshop series that promotes technical proficiency in software development and machine learning.The software tool, developed as part of this project, will allow astronomers to more easily access Bayesian statistical methods to interpret image data from astronomical surveys. Bayesian methods excel at uncertainty quantification and data integration, two capabilities that will be critical in analyzing the deluge of data produced by next-generation astronomical surveys. One major barrier to the more widespread adoption of Bayesian analysis for interpreting astronomical images is computational: Bayesian inference is notoriously computationally demanding. A second major barrier is social: up to now, novel Bayesian methods have been developed in isolation by statisticians and have rarely been integrated into astronomy workflows because it is unclear to practitioners in either discipline how this can be accomplished. The BLISS Project addresses both these computational and community integration challenges. To overcome the computational challenges, BLISS leverages recent advances in Bayesian inference methodology, including the use of deep learning, variational inference, and GPU acceleration. To ensure immediate and sustainable community use, development of the BLISS is guided by needs identified by domain experts, who are themselves prepared to participate in BLISS's development and are enthusiastic about integrating BLISS into their teams' data analysis workflows.This project is supported by the Office of Advanced Cyberinfrastructure in the Directorate for Computer & Information Science & Engineering, the Division of Mathematical Sciences and the Division of Astronomical Sciences in the Directorate for Mathematical and Physical Sciences.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.

Bliss（Bayesian Light Source分离器）项目是一项跨学科研究工作，旨在开发一种软件工具，允许天文学家利用机器学习的最新进展。通过利用这些进步，天文学家可以快速分析大量复杂数据以了解我们宇宙的性质。该项目还通过研讨会系列来吸引并教育更广泛的受众，该系列促进了软件开发和机器学习方面的技术水平。作为该项目的一部分，该软件工具将使天文学家更容易访问贝叶斯统计方法，从而从天文学调查中解释图像数据。贝叶斯方法在不确定性量化和数据整合方面表现出色，这两个功能对于分析下一代天文调查产生的数据造成的泛滥至关重要。贝叶斯分析用于解释天文图像的贝叶斯分析更广泛地采用的一个主要障碍是计算：贝叶斯推论众所周知，计算要求。第二个主要的障碍是社会：到目前为止，统计学家孤立地开发了新颖的贝叶斯方法，并且很少将其整合到天文学工作流程中，因为在任何一学科中，从业人员尚不清楚如何实现这一目标。 Bliss项目既应对这些计算和社区整合挑战。为了克服计算挑战，幸福感利用了贝叶斯推理方法的最新进展，包括使用深度学习，变分推断和GPU加速。为了确保立即和可持续的社区使用，幸福的发展受到领域专家确定的需求的指导，他们本身准备参加Bliss的发展，并热衷于将Bliss集成到团队的数据分析工作流程中。该项目由高级网络结构办公室提供支持，该项目在高级网络局中，在计算机，信息科学和工程学局中，该部门的数学科学局和分区，该项目是数学上的分区，该项目是数学上的分区。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标准通过评估来支持的。