Stacking machine learning classifiers to identify Higgs bosons at the LHC

Stacking machine learning classifiers to identify Higgs bosons at the LHC

Author Alves, A. Autor UNIFESP Google Scholar
Abstract Machine learning (ML) algorithms have been employed in the problem of classifying signal and background events with high accuracy in particle physics. In this paper, we compare the performance of a widespread ML technique, namely, stacked generalization, against the results of two state-of-art algorithms: (1) a deep neural network (DNN) in the task of discovering a new neutral Higgs boson and (2) a scalable machine learning system for tree boosting, in the Standard Model Higgs to tau leptons channel, both at the 8 TeV LHC. In a cut-and-count analysis, stacking three algorithms performed around 16% worse than DNN but demanding far less computation efforts, however, the same stacking outperforms boosted decision trees. Using the stacked classifiers in a multivariate statistical analysis (MVA), on the other hand, significantly enhances the statistical significance compared to cut-and-count in both Higgs processes, suggesting that combining an ensemble of simpler and faster ML algorithms with MVA tools is a better approach than building a complex state-of-art algorithm for cut-and-count.
Keywords Analysis and statistical methods
Pattern recognition
cluster finding
calibration and fitting methods
xmlui.dri2xhtml.METS-1.0.item-coverage Bristol
Language English
Sponsor CNPq
Grant number CNPq: 307098/2014-1
FAPESP: 2013/22079-8
Date 2017
Published in Journal Of Instrumentation. Bristol, v. 12, p. -, 2017.
ISSN 1748-0221 (Sherpa/Romeo, impact factor)
Publisher Iop Publishing Ltd
Extent -
Access rights Closed access
Type Article
Web of Science ID WOS:000405076600005

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