Studying High-dimensional Supersymmetry Models with Neural Networks

POSTER

Abstract

This research project investigated the feasibility of using neural networks to more easily study high-dimensional supersymmetry models, using the phenomenological Minimal Supersymmetric Standard Model (pMSSM) as the test case. Facilities such as the Large Hadron Collider are currently conducting experiments to search for evidence of physics beyond the Standard Model (BSM); supersymmetry is one of these candidates. Direct methods of searching for evidence of supersymmetry models are intractable due to computational limitations. Bayesian Neural Networks (BNNs) were used to generate predictions directly from a point in the pMSSM parameter space without needing to simulate particle collisions. This work focused on predicting cross-sections resulting from 13TeV proton-proton collisions. The training data was generated fusing the SUSY-HIT and Prospino codes. Once trained, the BNN provides a function for high-energy physicists to more readily explore the parameter space of the pMSSM and other BSM models.

Authors

  • Alexander Karbo

    Davidson Coll

  • S.S. Gupta

    Department of Applied Mathematics & Sciences, Khalifa University, Abu Dhabi 127788, UAE, Physics Department, Kalamazoo College, Kalamazoo, Michigan 49006, USA, National Superconducting Cyclotron Laboratory, Michigan State University, East Lansing, Michigan 48824, USA, Indian Institute of Technology Ropar, Nangal Road, Rupnagar (Ropar), Punjab 140 001, India, The Institute for Nuclear Research, Moscow, Davidson Coll, Western kentucky University, Bowling Green, KY 42101, Naval Postgraduate School, Austin Peay State University, Univ of Tennessee, Knoxville, University of Nebraska, Rajarata University of Sri Lanka, University of West Georgia, Department of Physics, University of Alabama at Birmingham, Center for High Pressure Science and Technology Advanced Research, Northwestern Univ, Univ of Virginia, Western Kentucky University, Physics Dept. Bowling Green, KY, Department of Physics, The University of Texas-Rio Grande Valley, TX 78539, Western Kentucky University, Bowling Green, KY 42101, Western Kentucky University, School of Electronic and Electrical Engineering, University of Leeds, Leeds LS2 9JT, United Kingdom, Austin Peay State Univ, University of Pardubice, Clemson University, Appalachian State Univ, Department of Physics, University of West Georgia, Department of Geosciences, University of West Georgia, Department of Physics and Astronomy, Georgia State University, Francis Marion University, The Pennsylvania State University, Auburn University, Department of Physics & Astronomy, Louisiana State University, Baton Rouge, Louisiana, Department of Physics, Brigham Young University-Idaho, Rexburg, Idaho, Department of Physics,North Carolina State University, William Mong Institute of Nano Science and Technology, MSTD, Oak Ridge National Laboratory, Department of Physics and Astronomy, Vanderbilt University, Univ of Bristol, University of Alabama in Birmingham, Georgia Institute of Technology, Sandia National Laboratories, University of South Florida