NNPDF

NNPDF (Neural Network Parton Distribution Functions) is a method used in particle physics to understand how the building blocks of protons and neutrons—called partons (quarks and gluons)—are distributed within them. Using advanced machine learning, specifically neural networks, NNPDF analyzes experimental data to create flexible, unbiased models of these distributions. This approach reduces assumptions and uncertainties, providing more accurate predictions for high-energy physics experiments, such as those conducted at the Large Hadron Collider. Essentially, NNPDF helps scientists better understand the internal structure of protons and neutrons by leveraging modern data analysis techniques.