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 a CNN and a DNN output, and unsupervised learning based on a VAE. As use cases, we consider two physical situ...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SciPost
2022-11-01
|
| Series: | SciPost Physics Core |
| Online Access: | https://scipost.org/SciPostPhysCore.5.4.050 |
| Summary: | 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. |
|---|---|
| ISSN: | 2666-9366 |