PT Journal
AU Khosa, CK
   Sanz, V
   Soughton, M
TI A simple guide from machine learning outputs to statistical criteria in particle physics
SO Scipost Physics Core
JI SciPost Phys. Core
PY 2022
BP 050
EP 31pp
VL 5
IS 4
DI 10.21468/SciPostPhysCore.5.4.050
LA English
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.
ER