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

TL;DR

This paper compares various machine learning models, including a novel Physics-Informed Neural Network, for analyzing particle physics data, highlighting their accuracy, efficiency, and adherence to physical laws in classifying Higgs observables.

Contribution

It introduces and evaluates a Physics-Informed Neural Network for physics data analysis, demonstrating its potential alongside traditional ML models.

Findings

XGBoost is fastest and effective with limited data

Standard Neural Networks and PINNs achieve higher accuracy

Trade-offs exist between computational time and model complexity

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.