SciPostPhysCore_5_4_050.pdf (1.88 MB)
A simple guide from machine learning outputs to statistical criteria in particle physics
journal contribution
posted on 2023-06-10, 06:39 authored by Charanjit K Khosa, Veronica Sanz, Michael SoughtonIn 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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- Published
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- Published version
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SciPost Physics CoreISSN
2666-9366Publisher
Stichting SciPostExternal DOI
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5Page range
050 1-31Department affiliated with
- Physics and Astronomy Publications
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- Yes
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- Yes
Legacy Posted Date
2023-03-30First Open Access (FOA) Date
2023-03-30First Compliant Deposit (FCD) Date
2023-03-30Usage metrics
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