| Texto completo | |
| Autor(es): |
Alves, A.
Número total de Autores: 1
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| Tipo de documento: | Artigo Científico |
| Fonte: | Journal of Instrumentation; v. 12, MAY 2017. |
| Citações Web of Science: | 6 |
| Resumo | |
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. (AU) | |
| Processo FAPESP: | 13/22079-8 - Física de partículas e campos: modelo padrão e suas extensões |
| Beneficiário: | Adriano Antonio Natale |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |