Journal
NUCLEAR PHYSICS B
Volume 943, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.nuclphysb.2019.114613
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Funding
- National Natural Science Foundation of China (NNSFC) [11705093, 11675242]
- CAS Center for Excellence in Particle Physics (CCEPP)
- CAS Key Research Program of Frontier Sciences
- Key R&D Program of Ministry of Science and Technology [2017YFA0402200-04]
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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. (C) 2019 The Author(s). Published by Elsevier B.V.
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