Reconstructing the Kinematics of Deep Inelastic Scattering with Deep Learning

Understand

We introduce a method to reconstruct the kinematics of neutral-current deep inelastic scattering (DIS) using a deep neural network (DNN).

• Unlike traditional methods, it exploits the full kinematic information of both the scattered electron and the hadronic-final state, and it accounts for QED radiation by identifying events with radiated photons and event-level momentum imbalance.
• The method is studied with simulated events at HERA and the future Electron-Ion Collider (EIC).
• We show that the DNN method outperforms all the traditional methods over the full phase space, improving resolution and reducing bias.