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arXiv 2507.15768hep-phhep-ex
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利用微分编程实现强子结构的事件级分析

Toward an event-level analysis of hadron structure using differential programming

Kevin Braga, Markus Diefenthaler, Steven Goldenberg, Daniel Lersch, Yaohang Li, Jian-Wei Qiu, Kishansingh Rajput, Felix Ringer, Nobuo Sato, Malachi Schram

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AI总结：

本文提出局部正交逆变换采样（LOITS）算法，将理论计算与实验模拟统一到微分编程框架中，实现从事件级数据直接重建量子关联函数，并通过闭合测试验证了其有效性。

摘要：

从基本夸克和胶子自由度出发重建强子的内部性质，是核物理与粒子物理的一个核心目标。这一努力处于多项重大实验计划的核心位置，例如杰斐逊实验室12 GeV计划和即将建成的电子-离子对撞机。一个主要挑战是固有的逆问题：将来自碰撞事件的大规模观测数据转换为表征QCD理论中强子系统微观结构的基本量子关联函数（QCFs）。科学计算和机器学习的最新进展为利用深度学习技术应对这一挑战开辟了新途径。一个特别有前景的方向是将理论计算与实验模拟整合到一个统一框架中，从而能够直接从事件级信息重建QCFs。在这项工作中，我们引入了一种称为局部正交逆变换采样（LOITS）算法的微分采样方法。我们通过闭合测试验证了其性能，展示了利用生成对抗网络从采样事件中准确重建测试分布。LOITS算法为解决涉及QCFs的逆问题提供了一个核心构建模块，并在微分编程框架内实现了端到端的推理流程。

英文摘要：

Reconstructing the internal properties of hadrons in terms of fundamental quark and gluon degrees of freedom is a central goal in nuclear and particle physics. This effort lies at the core of major experimental programs, such as the Jefferson Lab 12 GeV program and the upcoming Electron-Ion Collider. A primary challenge is the inherent inverse problem: converting large-scale observational data from collision events into the fundamental quantum correlation functions (QCFs) that characterize the microscopic structure of hadronic systems within the theory of QCD. Recent advances in scientific computing and machine learning have opened new avenues for addressing this challenge using deep learning techniques. A particularly promising direction is the integration of theoretical calculations and experimental simulations into a unified framework capable of reconstructing QCFs directly from event-level information. In this work, we introduce a differential sampling method called the local orthogonal inverse transform sampling (LOITS) algorithm. We validate its performance through a closure test, demonstrating the accurate reconstruction of a test distribution from sampled events using Generative Adversarial Networks. The LOITS algorithm provides a central building block for addressing inverse problems involving QCFs and enables end-to-end inference pipelines within the framework of differential programming.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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