发表机构
Stony Brook University; University of Washington; Brookhaven National Laboratory（石溪大学; 华盛顿大学; 布鲁克海文国家实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用机器学习对电子-离子对撞机全事件进行异常检测，通过弱监督和生成模型增强新物理信号搜索灵敏度，并验证了无监督图自编码器的补充作用。
AI 中文摘要
未来的电子-离子对撞机（EIC）将为寻找标准模型之外的新物理提供新的机会。我们研究利用机器学习和事件的完整粒子内容进行共振异常检测。我们考虑一个新的中性规范玻色子和一个重中性轻子作为具有不同电子-喷注拓扑的示例信号。弱监督学习利用这些事件信息来增强不变质量搜索的灵敏度，而无需在训练过程中识别单个信号事件。与高层面观测量（high-level observables）的比较表明，电子运动学占据了分离能力的重要部分，而粒子之间的关联提供了进一步的灵敏度。我们还通过调整一个在LHC喷注上预训练的模型来构建条件全事件生成器，以产生EIC背景事件。对信号占比（signal fractions）的扫描表明，利用学习到的背景参考可以实现显著的信号增强，为这种方法在EIC上的应用提供了概念验证。最后，我们考虑了一种完全无监督的图自编码器，它提供了适度的信号增强，但在信号占比过小而无法进行有效弱监督时仍能保持灵敏度。我们的结果推动了全事件异常检测作为EIC物理计划的一部分，并指出了开发适应其独特运动学的方法的方向。
英文摘要
The future Electron-Ion Collider (EIC) will offer new opportunities to search for physics beyond the Standard Model. We study resonant anomaly detection using machine learning and the full particle content of an event. We consider a new neutral gauge boson and a heavy neutral lepton as illustrative signals with different electron-jet topologies. Weakly supervised learning uses this event information to enhance the sensitivity of an invariant-mass search without identifying individual signal events during training. Comparisons with high-level observables show that the electron kinematics account for an important part of the separation, while correlations among the particles provide further sensitivity. We also construct a conditional full-event generator by adapting a model pretrained on LHC jets to produce EIC background events. A scan over signal fractions demonstrates substantial signal enhancement using the learned background reference, providing a proof of concept for this approach at the EIC. Finally, we consider a fully unsupervised graph autoencoder that provides modest signal enhancement but retains sensitivity at signal fractions too small for effective weak supervision. Our results motivate full-event anomaly detection as part of the EIC physics program and identify directions for developing methods suited to its distinct kinematics.
Comments42 pages, 17 figures
