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Supervised deep learning in high energy phenomenology: a mini review
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abstract
Deep learning, a branch of machine learning, have been recently applied to high energy experimental and phenomenological studies. In this note we give a brief review on those applications using supervised deep learning. We first describe various learning models and then recapitulate their applications to high energy phenomenological studies. Some detailed applications are delineated in details, including the machine learning scan in the analysis of new physics parameter space, the graph neural networks in the search of top-squark production and in the $CP$ measurement of the top-Higgs coupling at the LHC.
Forward citations
Cited by 3 Pith papers
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The LHC sensitivity to weak gauginos in light of the latest muon $g-2$ and dark matter results
In the MSSM, after muon g-2 and dark matter constraints, only bino-like LSPs with wino or slepton coannihilation survive, and a 27 TeV HE-LHC could probe most of the remaining mass range.
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On the coverage of neutralino dark matter in coannihilations at the upgraded LHC
Projected HE-LHC searches could rule out neutralino dark matter up to 2.6, 1.7, and 0.8 TeV in gluino, stop, and wino coannihilation scenarios, but not in stau coannihilation.
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