Reconstructing the kinematics of deep inelastic scattering with deep learning

@article{Arratia2021ReconstructingTK,
  title={Reconstructing the kinematics of deep inelastic scattering with deep learning},
  author={M. Arratia and D. Britzger and Owen Long and Benjamin Philip Nachman},
  journal={Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment},
  year={2021}
}
  • M. Arratia, D. Britzger, +1 author B. Nachman
  • Published 11 October 2021
  • Physics
  • Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
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… 

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