贝叶斯神经网络用于快速SUSY预测
Bayesian Neural Networks for Fast SUSY Predictions
摘要：
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。对新物理的探索通常由BSM理论指导，这些理论依赖于许多未知参数，在某些情况下，这使得测试它们的预测变得困难。在本文中，机器学习是用来模拟的映射从参数空间的现象学最小超对称标准模型（pMSSM），BSM理论与19个自由参数，它的一些预测。贝叶斯神经网络用于预测任意pMSSM参数点的截面，相关的最轻的中性希格斯玻色子玻色子的质量，和参数点的理论可行性。所有这三个量的平均百分误差为3.34%或更少，并在一个显着的时间短于可能的超对称代码的结果是来自建模。这些结果进一步证明了机器学习精确建模从BSM理论的高维空间到其预测的映射的潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
