Machine Learning for Predicting the Proton Structure Function $F_2^P$ in QCD

Abstract

We present a comparative study of four supervised machine learning regression algorithms -- Support Vector Regression (SVR), Gradient Boosting Regression (GBR), Gaussian Process Regression (GPR), and Multilayer Perceptron (MLP) -- for predicting the proton structure function $F_2^p(x, Q^2)$ using high-precision BCDMS experimental data. Unlike conventional methods that solve the DGLAP evolution equations, our data-driven framework directly captures the complex nonlinear dynamics of partonic structure. To ensure statistical robustness, we employ $k$-fold cross-validation and perform thorough hyperparameter optimization. Our results show that the MLP and GPR models achieve superior predictive accuracy. In particular, MLP exhibits the highest sensitivity to nonlinear gradients, while SVR proves most stable against experimental uncertainties. The close convergence of training and validation metrics confirms that the models learn the underlying QCD physics without overfitting to statistical fluctuations. This work highlights the potential of ML-based regression as a complementary tool for structure function analysis and kinematic extrapolation in high-energy physics.