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

TL;DR

This paper presents a novel ensemble deep learning approach combining random forest and autoencoders to improve Higgs boson signal detection amid background noise, achieving high discrimination accuracy.

Contribution

It introduces a new ensemble method integrating random forest and autoencoders for robust Higgs signal classification, enhancing detection performance.

Findings

Achieved AUC of 0.9 on private leaderboard

Attained an AMS score of 3.429

Demonstrated effective discrimination between Higgs signals and background noise

Abstract

Higgs boson is a fundamental particle, and the classification of Higgs signals is a well-known problem in high energy physics. The identification of the Higgs signal is a challenging task because its signal has a resemblance to the background signals. This study proposes a Higgs signal classification using a novel combination of random forest, auto encoder and deep auto encoder to build a robust and generalized Higgs boson prediction system to discriminate the Higgs signal from the background noise. The proposed ensemble technique is based on achieving diversity in the decision space, and the results show good discrimination power on the private leaderboard; achieving an area under the Receiver Operating Characteristic curve of 0.9 and an Approximate Median Significance score of 3.429.