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Author  |
Khosa, C.K.; Sanz, V.; Soughton, M. |
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Title |
A simple guide from machine learning outputs to statistical criteria in particle physics |
Type |
Journal Article |
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Year |
2022 |
Publication |
Scipost Physics Core |
Abbreviated Journal |
SciPost Phys. Core |
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Volume |
5 |
Issue |
4 |
Pages |
050 - 31pp |
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Abstract |
In this paper we propose ways to incorporate Machine Learning training outputs into a study of statistical significance. We describe these methods in supervised classification tasks using a CNN and a DNN output, and unsupervised learning based on a VAE. As use cases, we consider two physical situations where Machine Learning are often used: high-pT hadronic activity, and boosted Higgs in association with a massive vector boson. |
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Address |
[Khosa, Charanjit Kaur] Univ Bristol, HH Wills Phys Lab, Tyndall Ave, Bristol BS8 1TL, Avon, England, Email: Charanjit.Kaur@bristol.ac.uk; |
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Publisher |
Scipost Foundation |
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Language |
English |
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Notes |
WOS:000929724800002 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5475 |
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