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

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 CNN and DNN output, unsupervised learning based on VAE. As use cases, consider two physical situations where are often used: high- p_T pT hadronic activity, boosted Higgs association with massive vector boson.