Search for a Higgs portal scalar decaying to electron-positron pairs in the MicroBooNE detector
(2021)
Supernova neutrino burst detection with the Deep Underground Neutrino Experiment
The European Physical Journal C SpringerOpen 81:5 (2021) 423
Abstract:
We investigate the feasibility of using deep learning techniques, in the form of a one-dimensional convolutional neural network (1D-CNN), for the extraction of signals from the raw waveforms produced by the individual channels of liquid argon time projection chamber (LArTPC) detectors. A minimal generic LArTPC detector model is developed to generate realistic noise and signal waveforms used to train and test the 1D-CNN, and evaluate its performance on low-level signals. We demonstrate that our approach overcomes the inherent shortcomings of traditional cut-based methods by extending sensitivity to signals with ADC values below their imposed thresholds. This approach exhibits great promise in enhancing the capabilities of future generation neutrino experiments like DUNE to carry out their low-energy neutrino physics programsConvolutional neural network for multiple particle identification in the MicroBooNE liquid argon time projection chamber
Physical Review D American Physical Society (APS) 103:9 (2021) 092003
Measurement of the Longitudinal Diffusion of Ionization Electrons in the MicroBooNE Detector
(2021)
Measurement of the atmospheric muon rate with the MicroBooNE Liquid Argon TPC
Journal of Instrumentation IOP Publishing 16:04 (2021) p04004