arXiv subjects

Benjamin Jobilal

Publications and source records attributed to Benjamin Jobilal.

2 recordsLinked to original sources

arXiv2026

A virtual reality extension for the Geant4 toolkit

Geant4 is a toolkit for simulating particle interactions in matter that is widely used in particle physics, nuclear physics, medical imaging, and astrophysics. At present, Geant4 visualizations are generally viewed on a conventional, two-dimensional computer display. To enhance immersion, we have developed G4VR, a virtual reality (VR) application that extends Geant4 with VR capabilities, providing a more intuitive and comprehensive way to understand these interactions. The user running G4VR in a headset can get detailed information about individual tracks, interactions, and energy depositions by selecting them with the hand controllers. G4VR also allows the user to examine the chronology of interactions, and includes animations of entire events as immersive movies. G4VR includes built-in event displays of several types that allow a first-time user to easily explore its various functionalities. To interface with Geant4, we have developed a new visualization driver called G4XR that allows direct integration with the Geant4 source code. This enables any Geant4 experiment to be loaded and experienced in VR seamlessly.

hep-ex↗

arXiv2026

Quantum Graph Neural Networks for Jet Tagging on Quantum Hardware

Jets are central to the physics programs of both current and future colliders, from precision Standard Model measurements and searches for new physics at the Large Hadron Collider to studies of nucleon structure at the future Electron-Ion Collider. Motivated by these applications, we explore quantum machine learning for jet classification and present a permutation-invariant Quantum Graph Neural Network (QGNN) applied to particle-cloud representations of jets. We apply the model to two such discrimination tasks: quark vs. gluon and up vs. down quark flavor tagging, with the latter being, to our knowledge, the first application of a quantum model to this problem. In the ideal simulation, the QGNN performs competitively against the Particle Flow Network and traditional QCD observables. We further deploy scaled-down models to IBM and IonQ quantum processing units (QPUs), where we train and evaluate them, obtaining promising results. Finally, we perform an interpretability analysis to characterize the observables learned by the quantum model, relating them to generalized angularities for the quark-gluon study and to jet charge for the flavor study.

hep-ph↗