Determination of the HERA coherent diffractive $J/\psi$ production cross section via artificial neural network

TL;DR

This paper introduces a model-independent artificial neural network approach to analyze HERA data on coherent diffractive $J/ar{psi}$ production, enabling more reliable cross-section predictions and insights into the exponential slope $b$.

Contribution

The paper presents the first application of an ANN-based model-independent method to analyze diffractive $J/ar{psi}$ production data, improving prediction accuracy and extracting the slope parameter $b$.

Findings

ANN model accurately predicts differential cross-sections across kinematic ranges.

The exponential slope $b$ varies significantly with $Q^2$ and $W$.

Combining HERA and LHC data extends the predictive capability.

Abstract

An exclusive coherent diffractive $J/\psi$ production dataset from HERA, covering a large kinematic range in the photon virtuality $Q^2$, the squared momentum transfer $t$, and the photon-proton center-of-mass energy $W$, has been analyzed using various theoretical models with different approaches. In common model analyses, the inherent assumptions and limited kinematic applicability somewhat restrict the predictive power of the models, resulting in model-dependent prediction results. In this paper, we present our model-independent approach for the same reaction process and dataset, utilizing an artificial neural network (ANN) technique. The prediction of the best ANN model for the HERA differential cross-section dataset over a range of $W$, $Q^2$, and $t$ is obtained. We then extend the ANN model by combining the HERA and LHC data at various values of $W$ to predict the total photoproduction cross-section and demonstrate how to extract the exponential slope $b$. We find that the exponential slope $b$ strongly depends on $Q^2$ and $W$.