313 citations · 882 across the 27 of their papers we have counts for
28 papers
Graph Neural Network for Neutrino Physics Event Reconstruction
V Hewes, Adam Aurisano, Giuseppe Cerati +6
Liquid Argon Time Projection Chamber (LArTPC) detector technology offers a wealth of high-resolution information on particle interactions, and leveraging that information to its fu…
A Case Study on Parallel HDF5 Dataset Concatenation for High Energy Physics Data Analysis
Sunwoo Lee, Kai-yuan Hou, Kewei Wang +8
In High Energy Physics (HEP), experimentalists generate large volumes of data that, when analyzed, helps us better understand the fundamental particles and their interactions. This…
Quantum computing hardware for HEP algorithms and sensing
M. Sohaib Alam, Sergey Belomestnykh, Nicholas Bornman +37
Quantum information science harnesses the principles of quantum mechanics to realize computational algorithms with complexities vastly intractable by current computer platforms. Ty…
Simulations for the Development of Quantum Computational Devices for HEP
James B. Kowalkowski, Adam L. Lyon
In this white paper, we describe characteristics of tools for classical simulations of quantum computational devices appropriate for High Energy Physics applications.
Reconstruction of Large Radius Tracks with the Exa.TrkX pipeline
Chun-Yi Wang, Xiangyang Ju, Shih-Chieh Hsu +22
Particle tracking is a challenging pattern recognition task at the Large Hadron Collider (LHC) and the High Luminosity-LHC. Conventional algorithms, such as those based on the Kalm…
Accelerating the Inference of the Exa.TrkX Pipeline
Alina Lazar, Xiangyang Ju, Daniel Murnane +21
Recently, graph neural networks (GNNs) have been successfully used for a variety of particle reconstruction problems in high energy physics, including particle tracking. The Exa.Tr…