TY  - JOUR
AU  - Khosa, C. K.
AU  - Sanz, V.
AU  - Soughton, M.
PY  - 2022
DA  - 2022//
TI  - A simple guide from machine learning outputs to statistical criteria in particle physics
T2  - SciPost Phys. Core
JO  - Scipost Physics Core
SP  - 050
EP  - 31pp
VL  - 5
IS  - 4
PB  - Scipost Foundation
AB  - 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.
UR  - https://arxiv.org/abs/2203.03669
UR  - https://doi.org/10.21468/SciPostPhysCore.5.4.050
DO  - 10.21468/SciPostPhysCore.5.4.050
LA  - English
N1  - WOS:000929724800002
ID  - Khosa_etal2022
ER  -