Real-Time Anomaly Detection for Charge-Based Triggering in LArTPCs
- Nevis Labs, Columbia U.
Modern particle detectors, including liquid argon time projection chambers (LArTPCs), collect a vast amount of data, making it impractical to save everything for offline analysis. As a result, these experiments need to employ different down-selection techniques during data acquisition, referred to as triggering. In this talk, I will present a framework that would enable real-time, data-driven triggering for LArTPCs, using anomaly detection algorithms implemented on Field-Programmable Gate Arrays (FPGAs). Drawing on a study that makes use of collected charge data from the MicroBooNE LArTPC Public Dataset, I will discuss the overall performance of such algorithms and potential applications for future neutrino experiments.
- Research Organization:
- Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), High Energy Physics (HEP) (SC-25)
- DOE Contract Number:
- AC02-07CH11359
- OSTI ID:
- 2429217
- Report Number(s):
- FERMILAB-SLIDES-24-0150-V; oai:inspirehep.net:2816603
- Country of Publication:
- United States
- Language:
- English
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Real-time Anomaly Detection for Liquid Argon Time Projection Chambers
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Tue Dec 31 19:00:00 EST 2024
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OSTI ID:3010791