26 citations · 54 across the 6 of their papers we have counts for
6 papers
Optimizing the Hit Finding Algorithm for Liquid Argon TPC Neutrino Detectors Using Parallel Architectures
Sophie Berkman, Giuseppe Cerati, Kyle Knoepfel +5
Neutrinos are particles that interact rarely, so identifying them requires large detectors which produce lots of data. Processing this data with the computing power available is be…
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…
GPU-accelerated machine learning inference as a service for computing in neutrino experiments
Michael Wang, Tingjun Yang, Maria Acosta Flechas +7
Machine learning algorithms are becoming increasingly prevalent and performant in the reconstruction of events in accelerator-based neutrino experiments. These sophisticated algori…
Reconstruction for Liquid Argon TPC Neutrino Detectors Using Parallel Architectures
Sophie Berkman, Giuseppe Cerati, Brian Gravelle +3
Neutrinos are particles that interact rarely, so identifying them requires large detectors which produce lots of data. Processing this data with the computing power available is be…
Acceleration of matrix element computations for precision measurements
Oleg Brandt, Gaston Gutierrez, Michael H. L. S. Wang +1
The matrix element technique provides a superior statistical sensitivity for precision measurements of important parameters at hadron colliders, such as the mass of the top quark o…
BTeV Level 1 Vertex Trigger
Michael H. L. S. Wang
BTeV is a -physics experiment that expects to begin collecting data at the C0 interaction region of the Fermilab Tevatron in the year 2006. Its primary goal is to achieve unprec…