One of the goals of current particle physics research is to obtain evidence for new physics, that is, physics beyond the Standard Model (BSM), at accelerators such as the Large Hadron Collider (LHC) at CERN. The searches for new physics are often guided by BSM theories that depend on many unknown parameters, which, in some cases, makes testing their predictions difficult. In this paper, machine learning is used to model the mapping from the parameter space of the phenomenological Minimal Supersymmetric Standard Model (pMSSM), a BSM theory with 19 free parameters, to some of its predictions. Bayesian neural networks are used to predict cross sections for arbitrary pMSSM parameter points, the mass of the associated lightest neutral Higgs boson, and the theoretical viability of the parameter points. All three quantities are modeled with average percent errors of 3.34% or less and in a time significantly shorter than is possible with the supersymmetry codes from which the results are derived. These results are a further demonstration of the potential for machine learning to model accurately the mapping from the high dimensional spaces of BSM theories to their predictions.

中文翻译：

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用于快速 SUSY 预测的贝叶斯神经网络

当前粒子物理学研究的目标之一是在欧洲核子研究中心的大型强子对撞机 (LHC) 等加速器上获得新物理学的证据，即超越标准模型 (BSM) 的物理学。对新物理学的搜索通常由依赖于许多未知参数的 BSM 理论指导，在某些情况下，这使得测试他们的预测变得困难。在本文中，机器学习用于建模从现象学最小超对称标准模型 (pMSSM) 的参数空间到它的一些预测的映射，pMSSM 是一种具有 19 个自由参数的 BSM 理论。贝叶斯神经网络用于预测任意 pMSSM 参数点的横截面、相关的最轻中性希格斯玻色子的质量以及参数点的理论可行性。所有这三个量都以 3.34% 或更少的平均百分比误差进行建模，并且所用时间比导出结果的超对称代码可能的时间要短得多。这些结果进一步证明了机器学习的潜力，可以准确地模拟从 BSM 理论的高维空间到其预测的映射。