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Higgs boson is a fundamental particle, and the classification of Higgssignals is a well-known problem in high energy physics. The identification ofthe Higgs signal is a challenging task because its signal has a resemblance tothe background signals. This study proposes a Higgs signal classification usinga novel combination of random forest, auto encoder and deep auto encoder tobuild a robust and generalized Higgs boson prediction system to discriminatethe Higgs signal from the background noise. The proposed ensemble technique isbased on achieving diversity in the decision space, and the results show gooddiscrimination power on the private leaderboard; achieving an area under theReceiver Operating Characteristic curve of 0.9 and an Approximate MedianSignificance score of 3.429.

Extracting Signals of Higgs Boson From Background Noise Using Deep Neural Networks