Deeply learning deep inelastic scattering kinematics

We studied the use of machine learning to reconstruct deep inelastic scattering (DIS) kinematics in exclusive processes. In particular, we trained deep neural networks to reconstruct x, Q², and t, based on full information from ep scattering using CLAS 12 data from Jefferson Lab. These models were trained by a careful selection of Monte Carlo events. The results from the neural networks were compared to those of classical reconstruction methods, which only consider partial information from an event and, thusly, have limitations, including a sensitivity to initial and final state QED radiation, a requirement of precise energy measurements, and/or a poor resolution on different kinematic regions. The neural networks trained in our study reconstruct event kinematics based on the information used in the classical methods, but, in addition, are enhanced through correlations and patterns revealed in the simulated data sets from event generators.