ANN for Diffractive J/ψ Production at HERA

This paper uses an Artificial Neural Network (ANN) to analyze data from the HERA experiment on coherent diffractive J/ψ production. The authors aim to provide a model-independent analysis, overcoming limitations of traditional model-dependent approaches. They predict differential cross-sections and extend the model to include LHC data, extracting the exponential slope 'b' and analyzing its dependence on kinematic variables. This is significant because it offers a new, potentially more accurate, way to analyze high-energy physics data and extract physical parameters.