GPT-style model accelerates CLAS12 detector simulation

Researchers have developed a GPT-style autoregressive transformer model to simulate detector hits for the CLAS12 experiment at the Thomas Jefferson National Accelerator Facility. This model, conditioned on incident momentum, generates realistic detector hits across nine calorimeter layers, reproducing key physics characteristics. The generative approach achieves inference speeds over 700 events per second on a single GPU, significantly outperforming traditional Geant4-based simulations while maintaining essential physics fidelity for high-luminosity experiments. AI

IMPACT Accelerates scientific discovery by enabling faster, high-fidelity simulations for particle physics experiments.

RANK_REASON Academic paper detailing a novel application of GPT-style models for scientific simulation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• alphaXiv
• arXiv
• CatalyzeX
• CLAS12
• Cristiano Fanelli
• DagsHub
• Geant4
• generative pre-trained transformer
• Gotit.pub
• Hugging Face
• ScienceCast
• Thomas Jefferson National Accelerator Facility

• paper
• infra

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →