Reconstructing the Kinematics of Deep Inelastic Scattering with Deep Learning
| Title: | Reconstructing the Kinematics of Deep Inelastic Scattering with Deep Learning |
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| Authors: | Arratia, Miguel, Britzger, Daniel, Long, Owen, Nachman, Benjamin |
| Source: | Nuclear Inst. and Methods in Physics Research, A 1025 (2022) 166164 |
| Publication Year: | 2021 |
| Collection: | High Energy Physics - Experiment High Energy Physics - Phenomenology Nuclear Experiment |
| Subject Terms: | High Energy Physics - Experiment, High Energy Physics - Phenomenology, Nuclear Experiment |
| More Details: | 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. Comment: This is the published version, which was accepted for publication December 4, 2021 |
| Document Type: | Working Paper |
| DOI: | 10.1016/j.nima.2021.166164 |
| Access URL: | http://arxiv.org/abs/2110.05505 |
| Accession Number: | edsarx.2110.05505 |
| Database: | arXiv |
| DOI: | 10.1016/j.nima.2021.166164 |
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