A simple guide from Machine Learning outputs to statistical criteria

2022/03/07 by Charanjit K. Khosa, Khosa, Charanjit K., Verónica Sanz +4 · 1 citation
Computer Science · Physics and Astronomy · #Computational Physics and Python Applications #FOS: Physical sciences #High Energy Physics - Phenomenology (hep-ph) #High-Energy Particle Collisions Research #Particle physics theoretical and experimental studies #hep-ph

paper · pdf · doi:10.48550/arxiv.2203.03669

32 pages, 15 figures

arxiv created 2022/03/07 · openalex publication_date 2022/03/07 · arxiv updated 2022/03/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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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