About this document
GNNs for DHCAL Design and Performance by Mansi Surti is a document available to read on EtoBox.
This document explores the use of Graph Neural Networks (GNNs) for hadron energy reconstruction and Particle Identification (PID) in a Digital Hadronic Calorimeter (DHCAL) context. The study achieved over 50% classification efficiency for neutrons and pions, with the highest efficiency of 77% for protons, while demonstrating improved energy resolution even with coarser detector granularity. The findings suggest that GNNs can enhance the design and performance of future DHCAL systems, making them more cost-e
- Author
- Mansi Surti
- Language
- EN