用机器学习加速粒子探测器响应模拟，速度远超传统方法。
ML-based Fast Simulation of FARICH Responses
- 基于条件生成对抗网络，根据粒子参数生成真实感光子击中数据。
- 生成结果与蒙特卡洛方法一致，模拟速度提升数十倍以上。
- 适合高能物理仿真、实验设计及实时数据分析场景。
高能物理中的探测器响应快速模拟至关重要。传统蒙特卡洛方法虽是现代粒子物理仿真核心，但计算成本高昂。本文提出一种基于机器学习的FARICH（聚焦气凝胶环形切伦科夫探测器）响应快速模拟方法。给定粒子轨迹与动量，目标是生成探测器阵列上真实的光子击中样本。我们设计了一种轻量化卷积结构的条件生成对抗网络（cGAN），可依据粒子参数生成投影探测器响应。通过概率图和重建速度分布等指标，将cGAN与线性统计基线对比，结果显示其生成样本高度真实，且相比蒙特卡洛模拟显著提速。
原文摘要 · Abstract (English)
A fast simulation of the detector response is a vital task in high-energy physics (HEP). Traditional Monte-Carlo methods form the backbone of modern particle physics simulation software but are computationally expensive. We present a machine-learning-based approach to fast simulation of the Focusing Aerogel Ring Imaging Cherenkov (FARICH) detector response. Given a particle track and momentum, the goal is to generate realistic samples of photon hits on the detector matrix. We propose a conditional Generative Adversarial Network (cGAN) with a lightweight convolutional architecture that reproduces the projected detector response conditioned on particle parameters. We compare the cGAN against a linear statistical baseline using metrics applied to probability maps and to the reconstructed velocity distributions. The cGAN produces realistic samples and provides a significant speed-up over Monte-Carlo simulation.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。