5 papers
Detector-aware target definitions for full-event particle reconstruction
Katharina Schäuble, Alessandro Brusamolino, Dolores Garcia +1
Hit-level ML-based particle reconstruction methods have recently shown promising results. However, the reconstruction models are currently provided with targets that are unaware of…
AI Agents Can Already Autonomously Perform Experimental High Energy Physics
Eric A. Moreno, Samuel Bright-Thonney, Andrzej Novak +2
Large language model-based AI agents are now able to autonomously execute substantial portions of a high energy physics (HEP) analysis pipeline with minimal expert-curated input. G…
Machine Learning on Heterogeneous, Edge, and Quantum Hardware for Particle Physics (ML-HEQUPP)
Julia Gonski, Jenni Ott, Shiva Abbaszadeh +118
The next generation of particle physics experiments will face a new era of challenges in data acquisition, due to unprecedented data rates and volumes along with extreme environmen…
End-to-end event reconstruction for precision physics at future colliders
Dolores Garcia, Lena Herrmann, Gregor Krzmanc +1
Future collider experiments require unprecedented precision in measurements of Higgs, electroweak, and flavour observables, placing stringent demands on event reconstruction. The a…
Fine-tuning machine-learned particle-flow reconstruction for new detector geometries in future colliders
Farouk Mokhtar, Joosep Pata, Dolores Garcia +4
We demonstrate transfer learning capabilities in a machine-learned algorithm trained for particle-flow reconstruction in high energy particle colliders. This paper presents a cross…