26 citations · 41 across the 7 of their papers we have counts for
7 papers
Applications of Deep Learning to physics workflows
Manan Agarwal, Jay Alameda, Jeroen Audenaert +65
Modern large-scale physics experiments create datasets with sizes and streaming rates that can exceed those from industry leaders such as Google Cloud and Netflix. Fully processing…
Detector and Beamline Simulation for Next-Generation High Energy Physics Experiments
Sunanda Banerjee, D. N. Brown, David N. Brown +31
The success of high energy physics programs relies heavily on accurate detector simulations and beam interaction modeling. The increasingly complex detector geometries and beam dyn…
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…
Calibration of calorimetric measurement in a liquid argon time projection chamber
Tingjun Yang
The liquid argon time projection chamber provides high resolution event images and excellent calorimetric resolution for studying neutrino physics and searching for beyond standard…
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…
Direct Photon Results from CDF
Tingjun Yang
Direct (prompt) photon production is a field of very high interest in hadron colliders. It provides probes to search for new phenomena and to test QCD predictions. In this article,…
New Results from ArgoNeuT
Tingjun Yang
In this article, I discuss the latest results from ArgoNeuT, including inclusive charged current cross sections and studies of final-state interactions.