Advancing Physics Data Analysis through Machine Learning and Physics-Informed Neural Networks

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—includingNearestNeighbors,DecisionTrees, RandomForest,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
Higgsobservablesandessentialparameters.Throughthiscomprehensiveanalysis,weaimtoshowcasethe
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 andPhysics-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.