Record |
Author |
Khosa, C.K.; Sanz, V.; Soughton, M. |
Title |
A simple guide from machine learning outputs to statistical criteria in particle physics |
Type |
Journal Article |
Year |
2022 |
Publication |
Scipost Physics Core |
Abbreviated Journal |
SciPost Phys. Core |
Volume |
5 |
Issue |
4 |
Pages |
050 - 31pp |
Keywords |
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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. |
Address |
[Khosa, Charanjit Kaur] Univ Bristol, HH Wills Phys Lab, Tyndall Ave, Bristol BS8 1TL, Avon, England, Email: Charanjit.Kaur@bristol.ac.uk; |
Corporate Author |
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Thesis |
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Publisher |
Scipost Foundation |
Place of Publication |
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Editor |
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Language |
English |
Summary Language |
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Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
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ISBN |
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Medium |
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Area |
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Expedition |
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Conference |
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Notes |
WOS:000929724800002 |
Approved |
no |
Is ISI |
yes |
International Collaboration |
yes |
Call Number |
IFIC @ pastor @ |
Serial |
5475 |
Permanent link to this record |