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A simple guide from machine learning outputs to statistical criteria in particle physics

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posted on 2023-06-10, 06:39 authored by Charanjit K Khosa, Veronica Sanz, Michael Soughton
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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Publication status

  • Published

File Version

  • Published version

Journal

SciPost Physics Core

ISSN

2666-9366

Publisher

Stichting SciPost

Volume

5

Page range

050 1-31

Department affiliated with

  • Physics and Astronomy Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2023-03-30

First Open Access (FOA) Date

2023-03-30

First Compliant Deposit (FCD) Date

2023-03-30

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