Collaborative Research: Photometric redshifts via Bayesian functional data analysis
合作研究:通过贝叶斯函数数据分析进行光度红移
基本信息
- 批准号:1814778
- 负责人:
- 金额:$ 9.95万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2018
- 资助国家:美国
- 起止时间:2018-07-01 至 2023-06-30
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Many projects mapping the sky require precise estimates of the speed at which galaxies are receding. A wide-spread technique derives this number, the redshift, from carefully measuring galaxy colors, using an analysis called "photo-zs". Unfortunately, current estimation methods are not precise enough to achieve major survey science goals. The present team of astronomers and statisticians will improve both the precision and the reliability of photo-zs. This work will provide key enabling technology for large surveys in progress and in development, which represent a considerable investment in astronomy. This project will increase the return on that investment. The research requires innovation in both astronomy and statistics and the development of new statistical methods likely to have significant additional impact. The research will help to train a diverse population of students and postdocs in advanced statistics via summer schools and other special sessions, and historically, many such trainees have gone on to pursue careers in data science.Current and forthcoming automated digital sky surveys aim to push further into "precision cosmology" territory by meticulously mapping the distribution and properties of hundreds of millions of galaxies, and measuring the details of thousands of supernovae. This requires accurate and precise estimation of the redshifts of galaxies using broad-band photometric data, by the technique known as photometric redshifts, or photo-zs for short. Unfortunately, current estimation methods do not enable the most complete science return from these surveys. The present project unites astronomers and statisticians to improve the precision as well as the reliability of photo-zs, by modeling spectral energy distributions of galaxies, using Bayesian functional data analysis (FDA), an approach that emphasizes predictive modeling and thorough propagation of information and uncertainty across hierarchical, multi-stage discovery chains. The project will use a modular, hierarchical modeling framework and account for similarity and diversity, with both conventional parameterizations, and new data-driven parameterizations. Because this framework will produce probabilistic photo-z estimates, with possibly complex uncertainties, the team will also study how optimally to provide such estimates and to use them for cosmological science. Open-source implementations of their algorithms will be accelerated by graphics processing units where appropriate. The research will provide valuable inter-disciplinary training to a graduate student, while developing new statistical methods by innovatively combining FDA, machine learning, and high-performance computing.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.
许多绘制天空地图的项目都需要对星系后退的速度进行精确的估计。 一种广泛使用的技术通过仔细测量星系的颜色,使用一种叫做“photo-zs”的分析方法来得出这个数字,即红移。 不幸的是,目前的估计方法不够精确,无法实现主要的调查科学目标。 目前的天文学家和统计学家团队将提高photo-zs的精度和可靠性。 这项工作将为正在进行和发展中的大型调查提供关键的使能技术,这是对天文学的一项相当大的投资。 该项目将增加该投资的回报。 这项研究需要在天文学和统计学方面进行创新,并开发可能产生重大额外影响的新统计方法。 这项研究将有助于通过暑期学校和其他特别课程培训各种各样的学生和博士后,使他们掌握高级统计学,从历史上看,许多这样的学员已经继续从事数据科学的职业。目前和即将到来的自动数字天空调查旨在通过精心绘制数亿个星系的分布和属性,进一步推进“精确宇宙学”领域,测量数千颗超新星的细节。 这需要使用宽带光度数据,通过称为光度红移(简称photo-zs)的技术,准确而精确地估计星系的红移。 不幸的是,目前的估计方法无法从这些调查中获得最完整的科学回报。 本项目联合天文学家和统计学家,通过使用贝叶斯函数数据分析(FDA)对星系的光谱能量分布进行建模,提高photo-zs的精度和可靠性,这种方法强调预测建模和跨层次,多阶段发现链的信息和不确定性的彻底传播。 该项目将使用模块化的分层建模框架,并考虑到传统参数化和新的数据驱动参数化的相似性和多样性。 由于这个框架将产生概率photo-z估计,可能具有复杂的不确定性,该团队还将研究如何最佳地提供这种估计并将其用于宇宙学科学。 在适当的情况下,图形处理单元将加速其算法的开源实现。 该研究将为研究生提供有价值的跨学科培训,同时通过创新性地结合FDA、机器学习和高性能计算来开发新的统计方法。该奖项反映了NSF的法定使命,并通过使用基金会的智力价值和更广泛的影响审查标准进行评估,被认为值得支持。
项目成果
期刊论文数量(2)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Globally Optimal and Scalable N-way Matching of Astronomy Catalogs
全局最优且可扩展的天文目录 N 路匹配
- DOI:10.3847/1538-3881/ac6bf6
- 发表时间:2022
- 期刊:
- 影响因子:0
- 作者:Nguyen, Tu;Basu, Amitabh;Budavári, Tamás
- 通讯作者:Budavári, Tamás
Fast Globally Optimal Catalog Matching using MIQCP
使用 MIQCP 进行快速全局最优目录匹配
- DOI:10.3847/1538-3881/acf5e2
- 发表时间:2023
- 期刊:
- 影响因子:0
- 作者:Feitelberg, Jacob;Basu, Amitabh;Budavári, Tamás
- 通讯作者:Budavári, Tamás
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Tamas Budavari其他文献
Tamas Budavari的其他文献
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