| Issue |
EPJ Web Conf.
Volume 214, 2019
23rd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2018)
|
|
|---|---|---|
| Article Number | 06002 | |
| Number of page(s) | 7 | |
| Section | T6 - Machine learning & analysis | |
| DOI | https://doi.org/10.1051/epjconf/201921406002 | |
| Published online | 17 September 2019 | |
https://doi.org/10.1051/epjconf/201921406002
Machine Learning Techniques in the CMS Search for Higgs Decays to Dimuons
1
University of Florida,
PO Box 118440, Gainesville, FL 32611
USA
2 Corresponding author,
* e-mail: dimi@ufl.edu
Published online: 17 September 2019
With the accumulation of large collision datasets at a center-of-mass energy of 13 TeV, the LHC experiments can search for rare processes, where the extraction of signal events from the copious Standard Model backgrounds poses an enormous challenge. Multivariate techniques promise to achieve the best sensitivities by isolating events with higher signal-to-background ratios. Using the search for Higgs bosons decaying to two muons in the CMS experiment as an example, we describe the use of Boosted Decision Trees coupled with automated categorization for optimal event classification, bringing an increase in sensitivity equivalent to 50% more data.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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