Extracting Signals of Higgs Boson From Background Noise Using Deep Neural Networks
This addresses the challenge of Higgs signal identification for high energy physics researchers, but it appears incremental as it builds on existing ensemble and deep learning techniques.
The study tackled the problem of classifying Higgs boson signals from background noise in high energy physics by proposing an ensemble method combining random forest, autoencoder, and deep autoencoder, achieving an area under the ROC curve of 0.9 and an Approximate Median Significance score of 3.429.
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.