Bayesian Neural Networks for Fast SUSY Predictions

Abstract

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.

References in corpus (8)

• Deep Learning in Neural Networks: An Overview
• Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHC
• Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHC
• On the Convergence of Adam and Beyond
• Overall Objective Priors
• Calibrated Bayes, for Statistics in General, and Missing Data in Particular
• Lessons and Prospects from the pMSSM after LHC Run I: Neutralino LSP
• Applying Bayesian Neural Networks to Separate Neutrino Events from Backgrounds in Reactor Neutrino Experiments

Cited by in corpus (10)

• Searches for the BSM scenarios at the LHC using decision tree based machine learning algorithms: A comparative study and review of Random Forest, Adaboost, XGboost and LightGBM frameworks
• Efficient sampling of constrained high-dimensional theoretical spaces with machine learning
• Exploring Parameter Spaces with Artificial Intelligence and Machine Learning Black-Box Optimisation Algorithms
• Active learning BSM parameter spaces
• TensorBNN: Bayesian Inference for Neural Networks using Tensorflow
• Learning Uncertainties the Frequentist Way: Calibration and Correlation in High Energy Physics
• Combining Evolutionary Strategies and Novelty Detection to go Beyond the Alignment Limit of the $Z_3$ 3HDM
• Calibrating Bayesian Generative Machine Learning for Bayesiamplification
• Bayesian Active Search on Parameter Space: a 95 GeV Spin-0 Resonance in the ($B-L$)SSM
• Exploring Scotogenic Parameter Spaces and Mapping Uncharted Dark Matter Phenomenology with Multi-Objective Search Algorithms