Intelligent Triggers for Rare Event Detection in Liquid Argon Detectors
- Columbia U. (main)
Next-generation neutrino experiments like SBND and DUNE rely on Liquid Argon Time Projection Chambers (LArTPCs), which produce exceptionally detailed data at high volume. Capturing rare or unexpected events in real-time is a major challenge. Our project explores the use of machine learning, specifically autoencoder-based anomaly detection, to identify unusual activity directly from raw detector signals. Inspired by successes at the CMS experiment, we demonstrate that such methods can be adapted to LArTPCs and show promising results in both simulated studies and early steps toward real-time hardware deployment. This approach could open new avenues for detecting signals from physics beyond the Standard Model.
- Research Organization:
- Columbia U. (main)
- Sponsoring Organization:
- US Department of Energy
- DOE Contract Number:
- 89243024CSC000002
- OSTI ID:
- 2572227
- Report Number(s):
- FERMILAB-POSTER-25-0060-V; oai:inspirehep.net:2948096
- Journal Information:
- No journal information, Journal Name: No journal information
- Country of Publication:
- United States
- Language:
- English
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