| Record |
| Author |
Khosa, C.K.; Sanz, V.; Soughton, M. |
| Title |
A simple guide from machine learning outputs to statistical criteria in particle physics |
Type |
Journal Article |
| Year |
2022 |
Publication |
Scipost Physics Core |
Abbreviated Journal |
SciPost Phys. Core |
| Volume |
5 |
Issue |
4 |
Pages |
050 - 31pp |
| Keywords |
|
| 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. |
| Address |
[Khosa, Charanjit Kaur] Univ Bristol, HH Wills Phys Lab, Tyndall Ave, Bristol BS8 1TL, Avon, England, Email: Charanjit.Kaur@bristol.ac.uk; |
| Corporate Author |
|
Thesis |
|
| Publisher |
Scipost Foundation |
Place of Publication |
|
Editor |
|
| Language |
English |
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
|
ISBN  |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
|
| Notes |
WOS:000929724800002 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ pastor @ |
Serial |
5475 |
| Permanent link to this record |
