29 citations · 35 across the 6 of their papers we have counts for
6 papers
TIGER: A Topology-Agnostic, Hierarchical Graph Network for Event Reconstruction
Nathalie Soybelman, Francesco A. Di Bello, Nilotpal Kakati +1
Event reconstruction at the LHC, the task of assigning observed physics objects to their true origins, is a central challenge for precision measurements and searches. Many existing…
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…
Denoising Graph Super-Resolution towards Improved Collider Event Reconstruction
Nilotpal Kakati, Etienne Dreyer, Eilam Gross
In preparation for Higgs factories and energy-frontier facilities, future colliders are moving toward high-granularity calorimeters to improve reconstruction quality. However, the…
Configurable calorimeter simulation for AI applications
Francesco Armando Di Bello, Anton Charkin-Gorbulin, Kyle Cranmer +9
A configurable calorimeter simulation for AI (COCOA) applications is presented, based on the Geant4 toolkit and interfaced with the Pythia event generator. This open-source project…
Reconstructing particles in jets using set transformer and hypergraph prediction networks
Francesco Armando Di Bello, Etienne Dreyer, Sanmay Ganguly +8
The task of reconstructing particles from low-level detector response data to predict the set of final state particles in collision events represents a set-to-set prediction task r…
Set-Conditional Set Generation for Particle Physics
Francesco Armando Di Bello, Etienne Dreyer, Sanmay Ganguly +6
The simulation of particle physics data is a fundamental but computationally intensive ingredient for physics analysis at the Large Hadron Collider, where observational set-valued…