# ACED: Physics-informed Geometric Deep Learning for Astrophysical Neutrino Reconstruction in IceCube DeepCore

> **NSF 01002526DB NSF RESEARCH & RELATED ACTIVIT** · Georgia Tech Research Corporation (GA) · $487,503

## Abstract

Neutrinos are unique messengers, carrying information about the universe's most energetic astrophysical phenomena. Over the past decade, the IceCube Neutrino Observatory at the South Pole has made key discoveries by detecting high-energy neutrinos and identifying two active galaxies as neutrino sources. However, sub-TeV neutrinos (10–1000 GeV) remain a largely unexplored frontier with the potential to significantly expand our observation of the universe. This project leverages advanced artificial intelligence (AI) techniques to overcome computational challenges and improve the reconstruction of sub-TeV neutrinos using IceCube’s DeepCore subdetector. These advancements will enable detailed studies of astrophysical sources such as NGC 1068 at lower energy scales and pave the way for real-time public alerts of sub-TeV neutrinos, fostering coordinated follow-up observations across the electromagnetic spectrum. In addition to advancing neutrino astrophysics, the AI methodologies developed here will benefit a wide range of fields with similar challenges, including weather forecasting, neuroscience, smart cities, and precision farming, by enhancing the analysis of distributed sensor data. By integrating education initiatives, outreach programs, and undergraduate participation, this project promotes access to advanced research, supports STEM, and inspires the next generation of scientists and engineers.

This project addresses the computational barriers to sub-TeV neutrino reconst

## Key facts

- **NSF award ID:** 2435957
- **Awardee organization:** Georgia Tech Research Corporation (GA)
- **SAM.gov UEI:** EMW9FC8J3HN4
- **PI:** Pan Li
- **Primary program:** 01002526DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** Windows on the Universe (WoU)
- **Estimated total:** $487,503
- **Funds obligated:** $487,503
- **Transaction type:** Standard Grant
- **Period:** 07/01/2025 → 06/30/2027

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2435957

## Citation

> US National Science Foundation, Award 2435957, ACED: Physics-informed Geometric Deep Learning for Astrophysical Neutrino Reconstruction in IceCube DeepCore. Retrieved via AI Analytics 2026-06-07 from https://api.ai-analytics.org/grant/nsf/2435957. Licensed CC0.

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