Journal
COMMUNICATIONS IN THEORETICAL PHYSICS
Volume 71, Issue 8, Pages 955-990Publisher
IOP Publishing Ltd
DOI: 10.1088/0253-6102/71/8/955
Keywords
high energy physics; phenomenology; machine learning; deep learning
Categories
Funding
- National Natural Science Foundation of China [11705093, 11305049, 11675242, 11821505, 11851303]
- CAS Center for Excellence in Particle Physics (CCEPP)
- CAS Key Research Program of Frontier Sciences
- Key RAMP
- D Program of Ministry of Science and Technique [2017YFA0402200-04]
- Peng-Huan-Wu Theoretical Physics Innovation Center [11747601]
Ask authors/readers for more resources
Deep learning, a branch of machine learning, has 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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available