CNNBoosting: Convolutional neural network-boosting for classification of Z boson decay modes

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Abstract
Research on Z boson decay patterns helps validate Standard Model predictions and explore new physical phenomena. However, analyzing large datasets from proton–proton colliders presents significant challenges, as traditional methods often struggle with efficiency and accuracy. To address this, we propose CNNBoosting, a novel ensemble learning algorithm that combines multiple weak convolutional neural networks (CNNs) to enhance classification performance. CNNBoosting targets two specific Z-boson decay modes (Zee and Zmumu), improving stability and accuracy in classifying particle interactions. Using a Z-boson dataset from Kaggle, we train and evaluate CNNBoosting, achieving impressive results: 92.74% accuracy, 92.74% precision, 92.78% recall and 92.75% F1 score. Comparisons with existing classifiers, including KNN, Random Forest, XGBoost, MLP, SVM,VGG-16, ResNet-50 and Decision Tree, demonstrate the superior performance of CNNBoosting. This is the first application of a CNN model to this dataset, and our results confirm its effectiveness in classifying Z-boson decay patterns, offering an efficient tool for researchers.
Funding Information
  • Natural Science Foundation of Henan Province (232300420114)

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