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.
Arratia, Miguel, et al. "Reconstructing the kinematics of deep inelastic scattering with deep learning." Nuclear Instruments and Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment, vol. 1025, Dec. 2021. https://doi.org/10.1016/j.nima.2021.166164
Arratia, Miguel, Britzger, Daniel, Long, Owen, & Nachman, Benjamin (2021). Reconstructing the kinematics of deep inelastic scattering with deep learning. Nuclear Instruments and Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment, 1025. https://doi.org/10.1016/j.nima.2021.166164
Arratia, Miguel, Britzger, Daniel, Long, Owen, et al., "Reconstructing the kinematics of deep inelastic scattering with deep learning," Nuclear Instruments and Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment 1025 (2021), https://doi.org/10.1016/j.nima.2021.166164
@article{osti_1871017,
author = {Arratia, Miguel and Britzger, Daniel and Long, Owen and Nachman, Benjamin},
title = {Reconstructing the kinematics of deep inelastic scattering with deep learning},
annote = {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.},
doi = {10.1016/j.nima.2021.166164},
url = {https://www.osti.gov/biblio/1871017},
journal = {Nuclear Instruments and Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment},
issn = {ISSN 0168-9002},
volume = {1025},
place = {United States},
publisher = {Elsevier},
year = {2021},
month = {12}}
Nuclear Instruments and Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment, Journal Name: Nuclear Instruments and Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment Vol. 1025; ISSN 0168-9002
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