ANN for Diffractive J/ψ Production at HERA
Analysis
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.
Key Takeaways
- •Applies an ANN to analyze HERA data on diffractive J/ψ production.
- •Provides a model-independent approach to overcome limitations of existing methods.
- •Predicts differential cross-sections and extracts the exponential slope 'b'.
- •Demonstrates the dependence of 'b' on kinematic variables ($Q^2$ and $W$).
Reference
“The authors find that the exponential slope 'b' strongly depends on $Q^2$ and $W$.”