11 citations · 39 across the 7 of their papers we have counts for
4 papers · 1 filter
Physics at the Edge: Benchmarking Quantisation Techniques and the Edge TPU for Neutrino Interaction Recognition
Stefano Vergani, Hilary Utaegbulam, Michael Wang +4
This work presents a benchmark of quantisation techniques for convolutional neural networks applied to neutrino interaction recognition, motivated by the need for low-power inferen…
Extracting low energy signals from raw LArTPC waveforms using deep learning techniques -- A proof of concept
Lorenzo Uboldi, David Ruth, Michael Andrews +4
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…
Coded masks for imaging of neutrino events
M. Andreotti, P. Bernardini, A. Bersani +76
The capture of scintillation light emitted by liquid Argon and Xenon under molecular excitations by charged particles is still a challenging task. Here we present a first attempt t…
A deep-learning based raw waveform region-of-interest finder for the liquid argon time projection chamber
ArgoNeuT Collaboration, R. Acciarri, B. Baller +19
The liquid argon time projection chamber (LArTPC) detector technology has an excellent capability to measure properties of low-energy neutrinos produced by the sun and supernovae a…