用深度学习解析核结构，从重离子碰撞中提取精确密度信息
Ultra-Peripheral Collisions as a Nuclear-Structure Interferometer with Interpretable Multitask Deep Learning
- 构建可解释多任务网络，同时推断核密度、形变等多重结构特征
- 在锆-锆碰撞中成功分离衍射与干涉信号，实现高精度核密度成像
- 适合核物理、高能物理研究者，助力未来大型实验数据分析
精确掌握核结构对基础物理至关重要，但直接探测极为困难。超外围碰撞（UPCs）提供了一种飞米尺度的核成像方法：通过相干矢量介子光致产生产生衍射和双源干涉图案，直接编码核空间密度。然而将这些图案转化为定量约束面临逆问题挑战，受形变、中子皮、相位模糊及实验背景的耦合影响。本文提出一种可解释的多任务深度学习框架，将横向动量分布映射至多个核结构指标，并识别各推断的主导动力学区域。以^{96}_{40}Zr + ^{96}_{40}Zr体系中的相干J/ψ光致产生产为例，验证了该方法可有效分离衍射主导与干涉主导信息，生成适用于未来高亮度数据的分析就绪可观测量。
原文摘要 · Abstract (English)
Precise knowledge of nuclear structure is essential across fundamental physics, yet probing these structures is notoriously difficult. To address this challenge, ultra-peripheral collisions (UPCs) provide a femtoscopic tomography for imaging the atomic nucleus. UPCs offer a pristine electromagnetic pathway: coherent vector-meson photoproduction generates patterns of diffraction and two-source interference that directly encode the nuclear spatial density. Turning these patterns into quantitative constraints is, however, a challenging inverse problem, complicated by correlated sensitivities to deformation and neutron skin, phase smearing, and experimental backgrounds. Here we introduce an interpretable Multitask deep-learning framework that maps transverse momentum distributions to multiple nuclear-structure indicators simultaneously and identifies the kinematic regions driving each inference. We demonstrate the approach with coherent $J/ψ$ photoproduction in $^{96}_{40}\text{Zr} + ^{96}_{40}\text{Zr}$ collisions, showing that the learned features separate diffraction-dominated and interference-dominated information and provide analysis-ready observables for future high-luminosity data.
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