An Educational Approach to Higgs Boson Hunting Using Machine Learning Classification Algorithms on ATLAS Open Data

Abstract

In this study, the performance of several classification algorithms that are used to separate the H → ττ signal from background is investigated. The data set came from the publicly available ATLAS data, which was utilized for the Machine Learning (ML) competition. The data was obtained from a full ATLAS simulation and originated from proton-proton collisions. There are 250 thousand events in the data set, and 70% of them were used to train the algorithms. The primary objective of this research is to identify the signal events from the background events by using various ML methods in the context of high-energy physics. In order to discover a solution to the binary classification problem that was discussed earlier, six distinct classification algorithms were utilized. This article also compares the performance of these classification algorithms, including Linear Support Vector Machines (SVM), Radical SVM, Logistic Regression, K-Nearest Neighbours, XGBoost Classifier, and the AdaBoost Classifier. The best results were obtained using the XGBoost Classification method, which had an AUC of 0.84 ± 1.9 x 10-3 followed by the AdaBoost Classifier with an AUC of 0.82 ± 2.5 x 10-3.

Keywords

• LHC
• ATLAS Experiment
• Machine Learning
• XGBoost Classifier
• Binary Classification

Thanks

The machine learning calculations reported in this paper were fully performed at TUBITAK ULAKBIM, High Performance and Grid Computing Center.

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