Predicting Z Boson Decay Modes: Evaluating the Performance of Machine Learning and Deep Learning Techniques in Particle Physics

This study presents an application of machine learning and deep learning techniques to the problem of classifying the decay modes of the Z boson, a fundamental particle in the Standard Model of particle physics. We focus on two specific decay modes: Zee and Zmumu. Our dataset comprises simulated events with variables such as transverse momentum, pseudorapidity, and phi angle of the decay leptons. The data is preprocessed via a mapping method for categorical data and a MinMaxScaler for numerical data. We compare the performance of several classification models, including K-Nearest Neighbors (KNN), Weighted KNN, Random Forest Classifier, KDTree KNN, XGBoost, Multi Layer Perceptron, Support Vector Machine, Decision Tree Classifier, and a Deep Learning model based on a sequential neural network architecture. Our results indicate that the XGBoost provides the highest accuracy (81.42%) among the tested models. However, the Deep Learning model exhibits superior precision (84.14%), which is crucial in reducing false positives in the classification task. The study emphasizes the potential of machine learning and deep learning in high-energy physics and suggests directions for future research to further improve the classification performance.