🤖 AI Summary
This work addresses the binary classification problem of experimental feasibility for Higgs boson observables in high-energy physics. We systematically benchmark XGBoost, Random Forest, AdaBoost, Quadratic Discriminant Analysis (QDA), standard neural networks, and Physics-Informed Neural Networks (PINNs). To our knowledge, this is the first evaluation of XGBoost–PINN synergy in particle physics. We propose a novel PINN architecture explicitly embedding Higgs-specific physical constraints and introduce a two-stage modeling strategy: XGBoost performs rapid initial screening on limited data, while PINN delivers high-accuracy, physically consistent final classification. Results show that XGBoost achieves the fastest training, whereas PINN attains superior classification accuracy and strict adherence to physical conservation laws. The study quantitatively elucidates the fundamental trade-off among predictive accuracy, computational efficiency, and physical interpretability—highlighting the complementary strengths of data-driven and physics-guided learning in collider phenomenology.
📝 Abstract
In an era increasingly focused on green computing and explainable AI, revisiting traditional approaches in theoretical and phenomenological particle physics is paramount. This project evaluates various machine learning (ML) algorithms-including Nearest Neighbors, Decision Trees, Random Forest, AdaBoost, Naive Bayes, Quadratic Discriminant Analysis (QDA), and XGBoost-alongside standard neural networks and a novel Physics-Informed Neural Network (PINN) for physics data analysis. We apply these techniques to a binary classification task that distinguishes the experimental viability of simulated scenarios based on Higgs observables and essential parameters. Through this comprehensive analysis, we aim to showcase the capabilities and computational efficiency of each model in binary classification tasks, thereby contributing to the ongoing discourse on integrating ML and Deep Neural Networks (DNNs) into physics research. In this study, XGBoost emerged as the preferred choice among the evaluated machine learning algorithms for its speed and effectiveness, especially in the initial stages of computation with limited datasets. However, while standard Neural Networks and Physics-Informed Neural Networks (PINNs) demonstrated superior performance in terms of accuracy and adherence to physical laws, they require more computational time. These findings underscore the trade-offs between computational efficiency and model sophistication.