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 conditioned on incident momentum and generates realistic detector hits autoregressively across all nine calorimeter layers as sequences of strip, ADC, and TDC tokens. We demonstrate that the model faithfully reproduces hit multiplicity, spatial distributions, energy deposits, and the energy-momentum response of the electromagnetic calorimeter. The generator achieves inference rates exceeding 700 events per second on a single GPU, providing a substantial speedup over traditional Geant4-based simulations while maintaining physics fidelity essential for high-luminosity experimental programs.

翻译：现代粒子物理实验对快速、高保真探测器模拟的需求日益增长，原因是探测器组件性能提升，且后续计算需求已接近可用资源极限。近年来，深度生成模型作为传统蒙特卡洛方法的有前景替代方案而兴起，近期研究从大型语言模型和自监督下一标记预测方法中汲取灵感。本研究提出一种GPT风格的自回归Transformer应用，作为托马斯·杰斐逊国家加速器实验室CLAS12实验量热器的快速替代模型。该模型以入射动量作为条件，跨越全部九层量热器，以条、ADC和TDC标记序列的形式自回归生成真实的探测器命中。我们证明该模型能忠实地再现命中多重性、空间分布、能量沉积以及电磁量热器的能量-动量响应。该生成器在单GPU上实现超过700事件每秒的推理速率，较传统基于Geant4的模拟获得显著加速，同时保持对高亮度实验项目至关重要的物理保真度。