Stacking machine learning classifiers to identify Higgs bosons at the LHC

TL;DR

This paper evaluates the effectiveness of stacked generalization in particle physics for Higgs boson detection, showing it offers a good balance between performance and computational efficiency compared to deep neural networks and boosted decision trees.

Contribution

It demonstrates that stacking multiple machine learning algorithms improves Higgs boson detection in LHC data, providing a computationally efficient alternative to complex deep learning models.

Findings

Stacked classifiers outperform boosted decision trees in cut-and-count analysis.

Stacking approaches significantly increase statistical significance in multivariate analysis.

Stacked models are less accurate than deep neural networks but require less computation.

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, \emph{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, \emph{stacking} three algorithms performed around 16\% worse than DNN but demanding far less computation efforts, however, the same \emph{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.