Detecting and studying light echoes in the era of Rubin and Artificial Intelligence
Detecting and studying light echoes in the era of Rubin and Artificial Intelligence
批准号:
2108841
负责人:
Federica Bianco
金额:
$59.61万
依托单位:
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
项目状态:
已结题
起止时间:
2021-08-01 至 2024-07-31
中文摘要
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英文摘要
This award supports building an artificial intelligence (AI)-based pipeline for the all-sky-scale automated detection and study of light echoes. Light echoes (LEs) are caused by stellar explosions lighting up cosmic dust, and they are faint, rare, diffuse, and hard to detect. The detection of all sky samples of LEs can enable the discovery of previously unknown Galactic supernovae, and permit the study of Galactic dust and the history of stellar explosions and eruptions in the Galaxy. These new AI methods will robustly re-discover old, and discover new, light echoes in both existing and future survey datasets. The project will develop essential data science skills in students at the University of Delaware and at Delaware State University, a minority- and rural population-serving HBCU. AI model architectures for efficient and reliable detection of low-signal-to-noise diffuse features can be used throughout astronomy, for medical imaging, and in ecology and urban metabolism studies.Presently, LEs are detected by visual inspection, which is limiting and does not scale to all-sky surveys. The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will observe the southern sky at frequent intervals, making it an ideal LE survey. Unfortunately, the LSST alert pipeline is optimized for point sources and will entirely miss LEs. This project will produce the first pipeline for the automated all-sky detection and study of LEs by leveraging cutting-edge AI, and support deployment of the pipeline on the LSST science platform. The team will also be providing hands-on data- and computer-science training to students from groups historically underrepresented in the STEM fields, using an immersive learning program that includes Data Science boot camps, hackathons, and mentored research opportunities. This project capitalizes on the two NSF Big Ideas of “Harnessing the Data Revolution” and “Growing Convergence Research”.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.
期刊论文(1)
专著(0)
科研奖励(0)
会议论文
DOI:
10.3847/1538-3881/ac9409
发表时间:
2022-08
期刊:
The Astronomical Journal
影响因子:
--
作者:
[Xiaolong Li;F. Bianco;G. Dobler;Roee Partoush;A. Rest;Tatiana Acero-Cuellar;Riley Clarke;W. Fortino;S. Khakpash;Ming Lian]
通讯作者:
Xiaolong Li;F. Bianco;G. Dobler;Roee Partoush;A. Rest;Tatiana Acero-Cuellar;Riley Clarke;W. Fortino;S. Khakpash;Ming Lian
Every Datapoint Counts: Atmosphere-aided Flare Studies in the Rubin era
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批准号:2308016
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项目类别:Standard Grant
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资助金额:$24.87万
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财政年份:2023
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负责人:Federica Bianco
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依托单位:
Collaborative Research: HDR DSC: Delaware and Mid-Atlantic Data Science Corps
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批准号:2123264
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项目类别:Continuing Grant
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资助金额:$150.0万
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财政年份:2021
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负责人:Federica Bianco
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依托单位:
海外基金