Learning to reconstruct: a differentiable approach to muon tracking at the LHC
Reconstructing the trajectories of charged particles in high-energy collisions requires high precision to ensure reliable event reconstruction and accurate downstream physics analyses. In particular, both precise hit selection and transverse momentum estimation are essential to improve the overall r...
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| Autori principali: | , , , , |
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| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
IOP Publishing
2026-01-01
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| Serie: | Machine Learning: Science and Technology |
| Soggetti: | |
| Accesso online: | https://doi.org/10.1088/2632-2153/ae63a5 |
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