Machine Learning Techniques in the CMS Search for Higgs Decays to Dimuons

Abstract

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.