权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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.

布利斯（贝叶斯光源分离器）项目是一项跨学科的研究工作，旨在开发一种软件工具，使天文学家能够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的数据，以了解我们宇宙的性质。该项目还通过一系列研讨会吸引和教育更广泛的受众，以提高软件开发和机器学习的技术水平。作为该项目的一部分开发的软件工具将使天文学家能够更容易地使用贝叶斯统计方法来解释天文调查的图像数据。贝叶斯方法擅长于不确定性量化和数据集成，这两种能力对于分析下一代天文调查产生的大量数据至关重要。更广泛地采用贝叶斯分析来解释天文图像的一个主要障碍是计算性的：贝叶斯推理是众所周知的计算要求。第二个主要障碍是社会：到目前为止，新的贝叶斯方法已经被统计学家孤立地开发出来，很少被整合到天文学工作流程中，因为这两个学科的从业者都不清楚如何实现这一点。布利斯项目解决了这些计算和社区整合的挑战。为了克服计算挑战，布利斯利用贝叶斯推理方法的最新进展，包括使用深度学习，变分推理和GPU加速。为了确保社区的即时和可持续使用，布利斯的开发由领域专家确定的需求指导，他们自己准备参与布利斯的开发，并热衷于将布利斯整合到他们团队的数据分析工作流程中。该项目得到了计算机信息科学工程局高级网络基础设施办公室的支持&&，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。