发表机构
Department of Physics（物理系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本论文利用格点QCD和有效场论，从第一性原理计算部分子结构，包括TMD分布，并探索机器学习加速格点模拟，为精度强子物理提供非微扰输入。
AI 中文摘要
强子的内部结构由非微扰量子色动力学（QCD）支配。本论文利用格点QCD和有效场论，对部分子观测量进行了具有受控系统不确定性的第一性原理计算，从共线结构推进到编码强子三维部分子结构的横动量依赖分布（TMDs）。在大动量有效理论（LaMET）框架内，本工作展示了π介子分布振幅的最先进计算以及核子部分子分布的系统研究，并控制了重整化、激发态污染、傅里叶变换系统性和幂次修正。一种库仑规范下的准分布表述通过避免与Wilson线相关的线性发散简化了紫外结构，且在当前统计精度下，Gribov副本效应可忽略不计。基于这些进展，本论文报告了核子TMD部分子分布、Collins-Soper核、内禀软函数以及π介子TMD观测量的格点测定。这些结果为全局QCD分析和精度强子结构计划（包括电子-离子对撞机）提供了非微扰输入。与此同时，本工作探索了通过嵌入混合蒙特卡洛中的神经场变换来加速格点规范模拟的机器学习方法。在二维U(1)测试中，该方法降低了自相关并改善了向更细格距的性能，暗示了其在更高效格点QCD模拟中的潜在应用。
英文摘要
The internal structure of hadrons is governed by nonperturbative Quantum Chromodynamics (QCD). This dissertation presents first-principles calculations of partonic observables using lattice QCD and effective field theory, with controlled systematic uncertainties, advancing from collinear structure to transverse-momentum-dependent distributions (TMDs) that encode the three-dimensional partonic structure of hadrons. Within the large momentum effective theory (LaMET) framework, this work presents state-of-the-art calculations of pion distribution amplitudes and systematic studies of nucleon parton distributions, with control of renormalization, excited-state contamination, Fourier-transform systematics, and power corrections. A Coulomb-gauge formulation of quasi-distributions simplifies ultraviolet structure by avoiding Wilson-line related linear divergences, with Gribov-copy effects found to be negligible at current statistical precision. Building on these developments, this dissertation reports lattice determinations of nucleon TMD parton distributions, the Collins-Soper kernel, the intrinsic soft function, and pion TMD observables. These results provide nonperturbative inputs for global QCD analyses and the precision hadron-structure program, including the Electron-Ion Collider. In parallel, this work explores machine-learning acceleration of lattice gauge simulations through neural field transformations embedded in Hybrid Monte Carlo. In two-dimensional U(1) tests, the method reduces autocorrelation and improves performance toward finer lattice spacing, suggesting potential applications to more efficient lattice QCD simulations.
CommentsPhD dissertation
