Speaker
Description
The Deep Underground Neutrino Experiment (DUNE) will deploy massive liquid argon time projection chamber (LArTPC) detectors to measure the properties of neutrinos with unprecedented precision. Currently, two full-scale prototypes of the DUNE far detectors (FDs) are at CERN, which are called the ProtoDUNE detectors. Recent studies indicate that these prototypes could have the potential to detect neutrinos and long-lived beyond-standard model (BSM) particles from one of the targets in CERN’s north area that is exposed to the 400 GeV Super Proton Synchrotron (SPS) beam. A limiting factor in any neutrino and BSM physics program at ProtoDUNE is the ability to trigger for the interesting signal whilst rejecting the overwhelming cosmic-ray background present at these surface detectors. A new exclusive self-triggering algorithm has been developed for the DUNE data acquisition (DAQ) system that uses an XGBoost gradient boosted decision tree (GBDT) algorithm to select data with neutrino-like properties and reject cosmic-ray background. Simulations demonstrate that this algorithm improves the efficiency of selecting neutrino events at ProtoDUNE compared to existing algorithms. This talk will explain how such an ML-based triggering algorithm can enhance a future neutrino and BSM physics program at ProtoDUNE.
