4 papers
Using Graph Neural Networks for the segmentation of overlapping objects in high granularity calorimeters
Matthieu Melennec, Frédéric Magniette
High-granularity calorimeters at the High-Luminosity LHC require novel algorithms to resolve overlapping particle showers. We present an optimised Graph Neural Network (GNN) segmen…
The Calibr-A-Ton: a novel method for calorimeter energy calibration
Elena Vernazza, Jona Motta, Léa-Maria Rabour +7
The energy calibration of calorimeters at collider experiments, such as the ones at the CERN Large Hadron Collider, is crucial for achieving the experiments physics objectives. Sta…
Optimised Graph Convolution for Calorimetry Event Classification
Matthieu Melennec, Shamik Ghosh, Frédéric Magniette
In the recent years, high energy physics discoveries have been driven by the increasing of luminosity and/or detector granularity. This evolution gives access to bigger statistics…
Quantum Re-Uploading for Calorimetry: Optimized Architectures with Extended Expressivity
Léa Cassé, Bernhard Pfahringer, Albert Bifet +1
Near-term quantum machine learning must balance expressivity, optimization, and hardware constraints. We study quantum re-uploading units (QRUs) as compact circuits and compare the…