Exploring supersymmetry with machine learning

Ren, Jie (Department of Physics, Institute of Theoretical Physics, Nanjing Normal University, Nanjing, 210023, China) (CAS Key Laboratory of Theoretical Physics, Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing, 100190, China) (School of Physics, University of Chinese Academy of Sciences, Beijing, 100049, China) ; Wu, Lei (Department of Physics, Institute of Theoretical Physics, Nanjing Normal University, Nanjing, 210023, China) ; Yang, Jin Min (CAS Key Laboratory of Theoretical Physics, Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing, 100190, China) (School of Physics, University of Chinese Academy of Sciences, Beijing, 100049, China) (Department of Physics, Tohoku University, Sendai, 980-8578, Japan) ; Zhao, Jun (CAS Key Laboratory of Theoretical Physics, Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing, 100190, China) (School of Physics, University of Chinese Academy of Sciences, Beijing, 100049, China)

11 April 2019

Abstract: Investigation of well-motivated parameter space in the theories of Beyond the Standard Model (BSM) plays an important role in new physics discoveries. However, a large-scale exploration of models with multi-parameter or equivalent solutions with a finite separation, such as supersymmetric models, is typically a time-consuming and challenging task. In this paper, we propose a self-exploration method, named Machine Learning Scan (MLS), to achieve an efficient test of models. As a proof-of-concept, we apply MLS to investigate the subspace of MSSM and CMSSM and find that such a method can reduce the computational cost and may be helpful for accelerating the exploration of supersymmetry.


Published in: Nuclear Physics B (2019)
Published by: Elsevier
DOI: 10.1016/j.nuclphysb.2019.114613
License: CC-BY-3.0



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