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arXiv · 1708.06615

Exploring supersymmetry with machine learning

Jie Ren·Lei Wu·Jin Min Yang·Jun Zhao
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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.

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Jie Ren, Lei Wu, Jin Min Yang, Jun Zhao. 2017-08-22. Exploring supersymmetry with machine learning. https://arxiv.org/abs/1708.06615

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