Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Exploring supersymmetry with machine learning   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.

26
The categories of this paper are: bayes-an
16
The categories of this paper are: stat
14
The categories of this paper are: math-ph
33
The categories of this paper are: plasm-ph
34
The categories of this paper are: chem-ph
28
The categories of this paper are: alg-geom
8
The categories of this paper are: hep-ex
4
The categories of this paper are: cond-mat
22
The categories of this paper are: chao-dyn
5
The categories of this paper are: gr-qc
30
The categories of this paper are: q-alg
12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int
15
The categories of this paper are: nucl-ex
2
The categories of this paper are: math
29
The categories of this paper are: funct-an