GPT-Based Fast Simulation of CLAS12 Detector Hits via Conditional Autoregressive Generation
Modern particles physics experiments have demonstrated an increasing need for fast, high-fidelity detector simulation as detector components have improved and subsequent computational requirements approach the limits of available resources. Recently, deep generative models have emerged as a promising alternative to traditional Monte-Carlo methods, with recent works drawing inspiration from large language models (LLMs) and self-supervised next-token prediction methods. In this work, we present an application of a GPT-style autoregressive transformer as a fast surrogate model for the calorimeter inside the CLAS12 experiment at the Thomas Jefferson National Accelerator Facility. The model is...
| Authors | Cole Granger, James Giroux, Richard Tyson et al. |
|---|---|
| Primary category | physics.ins-det |
| Published | 14 June 2026 |
| Last updated at source | 14 June 2026 |
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