Machine Learning Approach in the Prediction of Differential Cross Sections and Structure Functions of Single Pion Electroproduction in the Resonance Region

This work explores artificial intelligence methods in the task of predicting differential cross sections in exclusive reactions of positively charged pion production induced by virtual photons. A fully connected neural network devoid of any prior theoretical knowledge about the scatterring process was trained on experimental data from the CLAS detector. We present a comparison of the network’s predictions with experimental data in the form of graphs showing the dependence of differential cross sections on kinematic variables in the excitation energy regions of nucleon resonances, as well as a comparison of the structure functions depending on the values of invariant mass of the final hadron system. Based on this algorithm we can interpolate both the cross-section values and structure function values in different regions of phase space. The neural network approach preserves all correlations of the multidimensional space of kinematic variables, it is model independent and does not consume any a priori knowledge of the process, it is easily extensible to a high dimensional space, which can serve as a good basis for building Monte Carlo event generators or detailed rection analysis.