@Article{Khosa_etal2022,
author="Khosa, C. K.
and Sanz, V.
and Soughton, M.",
title="A simple guide from machine learning outputs to statistical criteria in particle physics",
journal="Scipost Physics Core",
year="2022",
publisher="Scipost Foundation",
volume="5",
number="4",
pages="050--31pp",
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-pT hadronic activity, and boosted Higgs in association with a massive vector boson.",
optnote="WOS:000929724800002",
optnote="exported from refbase (https://references.ific.uv.es/refbase/show.php?record=5475), last updated on Sun, 05 Mar 2023 12:15:20 +0000",
doi="10.21468/SciPostPhysCore.5.4.050",
opturl="https://arxiv.org/abs/2203.03669",
opturl="https://doi.org/10.21468/SciPostPhysCore.5.4.050",
language="English"
}