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

Charanjit Kaur Khosa, Veronica Sanz, Michael Soughton

SciPost Phys. Core 5, 050 (2022) · published 2 November 2022

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-$p_T$ hadronic activity, and boosted Higgs in association with a massive vector boson.


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