| NSF Org: |
AST Division Of Astronomical Sciences |
| Recipient: |
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| Initial Amendment Date: | July 20, 2021 |
| Latest Amendment Date: | July 20, 2021 |
| Award Number: | 2108841 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Nigel Sharp
nsharp@nsf.gov (703)292-4905 AST Division Of Astronomical Sciences MPS Directorate for Mathematical and Physical Sciences |
| Start Date: | August 1, 2021 |
| End Date: | July 31, 2026 (Estimated) |
| Total Intended Award Amount: | $596,067.00 |
| Total Awarded Amount to Date: | $596,067.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
550 S COLLEGE AVE NEWARK DE US 19713-1324 (302)831-2136 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
Newark DE US 19716-2553 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | EXTRAGALACTIC ASTRON & COSMOLO |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.049 |
ABSTRACT
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.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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