2021/10/31 by M. Arratia, Miguel Arratia, D. Britzger +4
Earth and Planetary Sciences · Medicine · Physics and Astronomy · #Artificial intelligence #Classical mechanics #Computer science #Deep inelastic scattering #Deep learning #Geology #Inelastic scattering #Kinematics #Medical Imaging Techniques and Applications #Model Reduction and Neural Networks #Optics #Physics #Scattering #Seismic Imaging and Inversion Techniques #hep-ex #hep-ph #nucl-ex
paper · pdf · doi:10.1016/j.nima.2021.166164
published as Nuclear Inst. and Methods in Physics Research, A 1025 (2022) 166164 · This is the published version, which was accepted for publication December 4, 2021
openalex publication_date 2021/12/13 · arxiv created 2021/12/29 · arxiv updated 2022/01/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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. Our method has the potential to extend the kinematic reach of future experiments at the EIC, and thus their discovery potential in polarized and nuclear DIS.