发表机构
Beijing Normal University; Institute of Modern Physics, Chinese Academy of Science（北京师范大学; 中国科学院近代物理研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出人工智能辅助的局域红外相减框架，通过玻恩投影与有效场论匹配分离辐射项，由大型语言模型开发两种实现，无需切片参数即可重建次领头阶修正，并验证与 EERAD3 一致，展示了 AI 在高阶计算中的潜力。
AI 中文摘要
我们提出了基于人工智能开发的局域红外相减方法，该方法建立在到玻恩投影和有效场论匹配的基础上。该框架将可积辐射项与玻恩运动学下的有限贡献（称为玻恩接触项）分离。接触项通过分辨率观测量（如 N-喷注度 τ_N）中的有效场论奇异分布来确定。在人类物理指导下，一个大型语言模型开发了两种实现方案。一种使用神经网络进行相空间投影，并通过匹配有效场论累积量来拟合接触项。另一种采用解析构造，在固定玻恩动量的同时对辐射进行积分。它将有效场论 δ(τ_N) 系数与有限的四维辐射积分相结合，直接计算接触项。这给出了一个无需切片参数的局域相减公式，同时复用了现有的低阶辐射计算和有效场论奇异预测。作为演示，我们重建了正负电子湮灭中无质量三喷注和四喷注产生的完整次领头阶修正。我们还通过递归使用次领头阶投影到玻恩构造，并让大型语言模型设计机器学习控制以降低接触项积分的方差，尝试了次次领头阶双喷注产生。测试预测与 EERAD3 结果吻合良好。数值计算和投影网络训练使用 2020 年苹果 M1 MacBook，无 GPU 加速，展示了该构造在适度计算资源下的可行性。附录将局域相减扩展到三喷注次次领头阶，给出了显式辐射映射和提议的接触项公式。我们还展示了如何在保持玻恩动量固定的情况下对次次领头阶辐射进行积分，适用于任意数量的无质量末态喷注。我们的结果展示了人工智能如何通过构造红外相减并改进其数值积分来帮助高阶计算。
英文摘要
We present AI-developed local infrared subtraction, building on projection to Born and EFT matching. The framework separates an integrable radiation term from a finite contribution at Born kinematics, referred to as the Born contact. The contact is determined using the EFT singular distribution in a resolution observable such as N-jettiness $τ_N$. Under human physics guidance, an LLM develops two implementations. One uses a neural network for phase space projection and fits the contact by matching to EFT cumulants. The other uses an analytic construction that keeps the Born momenta fixed while integrating over radiation. It combines the EFT $δ(τ_N)$ coefficient with finite 4-dimensional radiation integrals to calculate the contact term directly. This gives a local subtraction formula without a slicing parameter, while reusing existing lower-order radiation calculations and EFT singular predictions. As a demonstration, we reconstruct the full NLO correction for massless 3- and 4-jet production in electron-positron annihilation. The attempt to the NNLO dijet production is also made by recursively using the NLO P2B construction with the LLM designing machine-learning controls to reduce the variance of the contact integral. The tested predictions are in good agreement with EERAD3. The numerical calculation and projection-network training use a 2020 Apple M1 MacBook, without GPU acceleration, illustrating the feasibility of the construction with modest computing resources. The appendices develop an extension of the local subtraction to 3-jet NNLO, giving explicit radiation maps and a proposed contact formula. We also show how to integrate over NNLO radiation while keeping the Born momenta fixed, for any number of massless final-state jets. Our results demonstrate how AI can help higher-order calculations by constructing infrared subtraction and improving its numerical integration.
Comments31 pages, 11 figs. References and text updated, including the analytic NNLO di-jet contact term calculated by the LLM directly within the P2B+EFT subtraction in 4 dimensions. Prompts and pseudocode for LLM-based agents to reproduce the figs are available in the Ancillary Files section. Prompts for reproducing the analytic contact term can be provided upon request
