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Research Spending & Results

Award Detail

Awardee:UNIVERSITY OF DELAWARE
Doing Business As Name:University of Delaware
PD/PI:
  • Federica B Bianco
  • (617) 777-4643
  • fbianco@nyu.edu
Co-PD(s)/co-PI(s):
  • Armin Rest
  • Austin J Brockmeier
Award Date:07/20/2021
Estimated Total Award Amount: $ 596,067
Funds Obligated to Date: $ 596,067
  • FY 2021=$596,067
Start Date:08/01/2021
End Date:07/31/2024
Transaction Type:Grant
Agency:NSF
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.049
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:Detecting and studying light echoes in the era of Rubin and Artificial Intelligence
Federal Award ID Number:2108841
DUNS ID:059007500
Parent DUNS ID:059007500
Program:EXTRAGALACTIC ASTRON & COSMOLO
Program Officer:
  • Nigel Sharp
  • (703) 292-4905
  • nsharp@nsf.gov

Awardee Location

Street:210 Hullihen Hall
City:Newark
State:DE
ZIP:19716-0099
County:Newark
Country:US
Awardee Cong. District:00

Primary Place of Performance

Organization Name:University of Delaware
Street:
City:Newark
State:DE
ZIP:19716-2553
County:Newark
Country:US
Cong. District:00

Abstract at Time of Award

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.

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