发表机构
University of Wisconsin–Madison（威斯康星大学麦迪逊分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出基于Transformer的强化学习架构LB-Explorer，搜索光滑Calabi-Yau三折叠上的heterotic线丛标准模型，通过离散Wilson线破缺SU(5)对称性，有效学习约束条件并过滤有效构型。
AI 中文摘要
我们提出了一种基于Transformer的强化学习架构“LB-Explorer”，用于搜索由光滑Calabi-Yau三折叠紧化产生的heterotic线丛标准模型。我们构造了具有$\ ext{SU}(5)$对称性的$E_8\ imes E_8$真空，其中$\ ext{SU}(5)$可通过离散Wilson线进一步破缺到标准模型规范群。我们在完全交Calabi-Yau流形上测试了LB-Explorer环境，尽管该神经网络架构自然推广到任何具有光滑单纯Mori锥和自由作用离散对称性的CY流形。LB-Explorer高效地学习线丛和的约束，保证$E_8$规范嵌入、反常消除、多稳定性（超对称）、谱的手征性以及不存在奇异物质。有效构型随后可通过施加缺失的约束（如线丛和的等变结构以及对粒子谱的进一步要求）进行过滤。为此，我们引入了一种结合CP-SAT求解器的混合架构，旨在通过扰动LB-Explorer找到的解来精确施加部分条件。LB-Explorer的通用性和可扩展性使其成为在具有大量模的弦景观中导航的强大工具。重现我们结果所需的代码和工具可在以下网址获取：https://github.com/...（此处为示例链接）
英文摘要
We propose a Transformer-based Reinforcement Learning architecture, "LB-Explorer", to search for heterotic line bundle standard models arising from compactifications on smooth Calabi-Yau (CY) threefolds. We focus on $E_8\times E_8$ heterotic string theory compactifications on CY with abelian line bundles to produce $\text{SU}(5)\times \text{S}(\text{U}(1)^5)$ symmetry, whose $\text{SU}(5)$ can be further broken to an MSSM-like gauge group using appropriate discrete Wilson lines. We test the LB-Explorer environment on complete intersection Calabi-Yau (CICY) manifolds, though the neural network architecture naturally generalizes to any CY admitting a simplicial Mori cone and a freely-acting discrete symmetry. The LB-Explorer efficiently learns constraints on the line bundle sums, guaranteeing the $E_8$ gauge embedding, anomaly cancellation, poly-stability (supersymmetry), chirality of the spectrum, and the absence of exotic matter. Valid configurations can be subsequently filtered by imposing the missing constraints, such as the equivariant structure of the line bundle sum and further requirements on the particle spectrum. In this direction, we introduce a hybrid architecture incorporating CP-SAT solvers that aims to impose some of the conditions exactly by perturbing solutions found by the LB-Explorer. The versatility and scalability of the LB-Explorer make it a powerful tool for navigating the string landscape with a large number of moduli. The code and tools necessary to reproduce our findings are available at https://github.com/alexmininno/LB-Explorer
Comments35 pages, many figures and long tables in the appendices. Code and tools available at https://github.com/alexmininno/LB-Explorer
