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DTSTART;TZID=America/New_York:20241118T113000
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UID:submissions.supercomputing.org_SC24_sess757_ws_ai4s115@linklings.com
SUMMARY:Machine Learning Aboard the ADAPT Gamma-Ray Telescope
DESCRIPTION:Ye Htet, Marion Sudvarg, Andrew Butzel, Jeremy Buhler, Roger C
 hamberlain, and James Buckley (Washington University in Saint Louis)\n\nTh
 e Advanced Particle-astrophysics Telescope (APT) is an orbital mission con
 cept designed to contribute to multi-messenger observations of transient p
 henomena in deep space. APT will be uniquely able to detect and accurately
  localize short-duration gamma-ray bursts (GRBs) in the sky in real time. 
 Current detection and analysis systems require resource-intensive ground-b
 ased computations; in contrast, APT will perform on-board analysis of GRBs
 , demanding analytical tools that deliver accurate results under severe si
 ze, weight, and power constraints.\n\nIn this work, we describe a neural n
 etwork approach in our computation pipeline for GRB localization, demonstr
 ating the capabilities of two neural networks: one to discard signals from
  background radiation, and one to estimate the uncertainty of GRB source d
 irection constraints associated with individual gamma-ray photons. We vali
 date the accuracy and computational efficiency of our networks using a phy
 sical simulation of GRB detection in the Antarctic Demonstrator for APT (A
 DAPT), a high-altitude balloon-borne prototype for APT.\n\nTag: Artificial
  Intelligence/Machine Learning\n\nRegistration Category: Workshop Reg Pass
 \n\nSession Chairs: Murali Emani (Argonne National Laboratory (ANL)); Gokc
 en Kestor (Barcelona Supercomputing Center; University of California, Merc
 ed); and Dong Li (University of California, Merced)\n\n
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