arXiv 2307.044502023-07-10hep-phhep-exnucl-th

面向 CLAS 双π光生过程生成式建模分析

Toward a generative modeling analysis of CLAS exclusive $2π$ photoproduction

T. Alghamdi, Y. Alanazi, M. Battaglieri, L. Bibrzycki, A. V. Golda, A. N. Hiller Blin, E. L. Isupov, Y. Li, L. Marsicano, W. Melnitchouk, V. I. Mokeev, G. Monta… 展开作者

T. Alghamdi, Y. Alanazi, M. Battaglieri, L. Bibrzycki, A. V. Golda, A. N. Hiller Blin, E. L. Isupov, Y. Li, L. Marsicano, W. Melnitchouk, V. I. Mokeev, G. Montana, A. Pilloni, N. Sato, A. P. Szczepaniak, T. Vittorini

备注：: 14 pages, 20 figures

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AI总结：

本文首次将生成对抗网络（GANs）用于高能实验物理中消除双π光生过程的探测器效应，并通过 CLAS g11 运动学条件下的闭包测试验证其能在多维相空间中保持运动学相关性，为真实数据分析奠定基础。

摘要：

人工智能支持的算法，尤其是生成模型，已在多种不同场景中得到成功应用。本工作首次证明生成对抗网络（GANs）可用于高能实验物理中，从多粒子末态中消除探测器效应，同时在多维相空间中保持运动学变量之间的相关性。我们在 Jefferson Lab CLAS g11 实验的运动学条件下，对由真实模型生成的双π光生伪数据进行了完整的闭包测试。导致相同末态的不同反应机制与 CLAS 探测器非平凡效应的重叠，构成了 AI 支持分析的理想测试案例。通过 bootstrap 方法进行的不确定性量化提供了与该过程相关的系统不确定性的估计。测试表明，即使训练数据集中存在探测器引起的畸变，GANs 也能重现高度相关的多维微分截面，并为将该框架应用于真实实验数据奠定了坚实基础。

英文摘要：

AI-supported algorithms, particularly generative models, have been successfully used in a variety of different contexts. In this work, we demonstrate for the first time that generative adversarial networks (GANs) can be used in high-energy experimental physics to unfold detector effects from multi-particle final states, while preserving correlations between kinematic variables in multidimensional phase space. We perform a full closure test on two-pion photoproduction pseudodata generated with a realistic model in the kinematics of the Jefferson Lab CLAS g11 experiment. The overlap of different reaction mechanisms leading to the same final state associated with the CLAS detector's nontrivial effects represents an ideal test case for AI-supported analysis. Uncertainty quantification performed via bootstrap provides an estimate of the systematic uncertainty associated with the procedure. The test demonstrates that GANs can reproduce highly correlated multidifferential cross sections even in the presence of detector-induced distortions in the training datasets, and provides a solid basis for applying the framework to real experimental data.

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