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.
@article{arxiv.2203.03669,
title = {A simple guide from Machine Learning outputs to statistical criteria},
author = {Charanjit K. Khosa and Veronica Sanz and Michael Soughton},
journal= {arXiv preprint arXiv:2203.03669},
year = {2022}
}