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arXiv · 2203.03669

A simple guide from Machine Learning outputs to statistical criteria

Charanjit K. Khosa·Veronica Sanz·Michael Soughton
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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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Charanjit K. Khosa, Veronica Sanz, Michael Soughton. 2022-03-07. A simple guide from Machine Learning outputs to statistical criteria. https://arxiv.org/abs/2203.03669

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