AI 中文总结
本研究提出两阶段机器学习策略，用于大型强子对撞机中标量与矢量轻夸克的区分，其自旋鉴别能力与发现潜力相当，无需更大数据集即可确定轻夸克自旋性质。
AI 中文摘要
我们提出了一种用于大型强子对撞机（LHC）轻夸克（LQ）信号表征的机器学习框架，重点是区分标量轻夸克（SLQ）和矢量轻夸克（VLQ）假设。该方法基于两阶段推理流水线，第一阶段训练分类器以区分标准模型（SM）背景与混合LQ信号，第二阶段分类器则用于区分SLQ和VLQ情景，利用第一阶段分类器推断的信号产额来指导对应的标量和矢量质量假设。从分类器输出构建检验统计量，并通过参考概率密度函数进行解释。该方法应用于含强子衰变τ轻子、多喷注及缺失横动量的真实LHC末态，通过模拟伪实验评估其性能。结果表明，所提策略在宽质量范围和信号强度下，为区分SLQ和VLQ信号提供了稳健且统计一致的流程；自旋鉴别能力与发现潜力密切相关，表明确定新发现LQ的自旋性质无需比发现所需数据集大得多的数据集。
英文摘要
We present a machine learning framework for the characterization of leptoquark (LQ) signals at the Large Hadron Collider, focusing on the discrimination between scalar (SLQ) and vector (VLQ) hypotheses. The method is based on a two-stage inference pipeline that combines a classifier trained to separate Standard Model backgrounds from a mixed LQ signal with a second classifier designed to distinguish between SLQ and VLQ scenarios, using the signal yield inferred from the first-stage classifier to guide the corresponding scalar and vector mass hypotheses. A test statistic is constructed from the classifier outputs and interpreted using reference probability density functions. The approach is applied to realistic LHC final states with hadronically decaying tau leptons, multiple jets, and missing transverse momentum, and its performance is assessed using simulated pseudo-experiments. We show that the proposed strategy provides a robust and statistically consistent procedure to discriminate between SLQ and VLQ signals across a wide range of masses and signal stregths. We find that the spin identification power closely follows the discovery potential, demonstrating that determining the spin nature of a newly discovered LQ does not require substantially larger datasets than those needed for discovery itself.
Comments36 pages, 13 figures, 4 tables. Code available at https://github.com/AndresDanielPerez/Two-Stage-ML-for-Scalar-and-Vector-Leptoquark-Discrimination
