将径迹拟合视为语言翻译问题
Track fitting as a language translation problem
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中文总结 AI 辅助
本研究将粒子径迹拟合建模为语言翻译问题，利用Transformer架构将探测器数据翻译为物理参数，在合成数据上精度优于回归方法，并能以98%准确率识别多径迹事件。
中文摘要 AI 辅助
粒子物理分析依赖于从探测器中的粒子径迹获取信息。传统上，这涉及收集单个探测器击中点，将其重组为径迹，并将其拟合到模型函数。大型语言模型（LLMs）在理解语言的语法和语义方面展现出了卓越的能力。我们的探索性研究利用Transformer将粒子径迹追踪概念化为一个语言翻译问题，即将探测器的语言翻译为物理学的语言。我们为此任务设计了一个带有自定义分词器的简单仅解码器架构，并且该模型在这种非典型环境中的行为提供了有趣的见解。我们评估了这种表述在径迹参数精度方面的优势，以及表示多径迹事件的便捷性，并最终在真实宇宙线缪子数据上测试了该模型。在合成数据集上，该模型在径迹参数精度上比简单回归技术高出一个数量级，同时与随机采样一致性（RANSAC）方法相当。该模型还以98%的准确率识别了多径迹事件。对真实宇宙线缪子事件的分析显示，天顶角分布与预期非常一致。我们的结果表明，一个紧凑的语言模型表现出有竞争力的性能，并具有表示多径迹事件的天然能力。需要进一步研究复杂几何结构，以充分理解该技术的潜力。
英文摘要
Particle physics analysis depends on information retrieved from particle tracks in a detector. Traditionally, this involves collecting individual detector hits, recombining them to form tracks, and fitting them to a model function. Large Language Models (LLMs) have demonstrated remarkable proficiency in comprehending the grammar and semantics of languages. Our exploratory study leverages transformers to conceptualise particle tracking as a language translation problem, wherein the language of detectors is translated into the language of physics. A simple decoder-only architecture with a custom tokenizer was designed for this task, and the behaviour of the model in this atypical setting provided interesting insights. We evaluated the advantages of this formulation in terms of track-parameter accuracy, and the ease of representing multi-track events, and finally tested the model on real cosmic-muon data. On synthetic datasets, the model outperformed a simple regression technique by an order of magnitude in track-parameter accuracy while being on par with the random sample consensus (RANSAC) method. The model also identified multi-track events with an accuracy of 98\%. The analysis of real cosmic-muon events showed an excellent agreement of the zenith angle distribution with expectations. Our results suggest that a compact language model shows competitive performance and a natural ability to represent multi-track events. Further studies with complex geometries are required to understand the full potential of this technique.
发表机构
- Central University of Karnataka（卡纳塔克邦中央大学）
机构由 AI 辅助整理，请以论文原文为准。
