Determining the form of the Higgs potential is one of the most exciting challenges of modern particle physics. Higgs pair production directly probes the Higgs self-coupling and should be observed in the near future at the High-Luminosity LHC. We explore how to improve the sensitivity to physics beyond the Standard Model through per-event kinematics for di-Higgs events. In particular, we employ machine learning through simulation-based inference to estimate per-event likelihood ratios and gauge potential sensitivity gains from including this kinematic information. In terms of the Standard Model Effective Field Theory, we find that adding a limited number of observables can help to remove degeneracies in Wilson coefficient likelihoods and significantly improve the experimental sensitivity.

翻译：确定希格斯势的具体形式是现代粒子物理学最激动人心的挑战之一。希格斯对产生过程直接探测希格斯自耦合，预计将在高亮度大型强子对撞机（HL-LHC）运行的近期被观测到。本研究探讨如何通过双希格斯事件的逐事例运动学信息提升对标准模型之外新物理的探测灵敏度。特别地，我们采用基于模拟推理的机器学习方法估计逐事例似然比，并评估引入运动学信息可能带来的灵敏度增益。在标准模型有效场论框架下，我们发现增加有限数量的可观测量有助于消除威尔逊系数似然函数中的简并性，从而显著提升实验灵敏度。