15 citations · 15 across the 4 of their papers we have counts for
4 papers
Exploring DHCAL design and performance with Graph Neural Networks
M. Borysova, D. Zavazieva, N. Kakati +2
In the context of a gas-sampling Digital Hadronic Calorimeter (DHCAL), we explore the potential of using Graph Neural Networks (GNN) for hadron energy reconstruction and Particle I…
Point Cloud Deep Learning Methods for Particle Shower Reconstruction in the DHCAL
Maryna Borysova, Shikma Bressler, Eilam Gross +2
Precision measurement of hadronic final states presents complex experimental challenges. The study explores the concept of a gaseous Digital Hadronic Calorimeter (DHCAL) and discus…
Design and optimization of a hadronic calorimeter based on micropattern gaseous detectors for a future experiment at the Muon Collider
Antonello Pellecchia, Marco Buonsante, Maryna Borysova +14
Micro-pattern gaseous detectors (MPGDs) are a promising readout technology for hadronic calorimeters (HCAL) thanks to their good space resolution, longevity and rate capability. We…
The Thick Gas Electron Multiplier and its derivatives: physics, technologies and applications
Shikma Bressler, Luca Moleri, Abhik Jash +2
The Thick Gas Electron Multiplier (THGEM) is a robust high-gain gas-avalanche electron multiplier - a building block of a variety of radiation detectors. It can be manufactured eco…