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Influence of Fake Track Rejection on Large-Radius Unified Flow Object Jet Reconstruction and the Development of a Deep Neural Network to Optimize High-p$_T$ Cluster Energy Extrapolation
This report summarizes my work studying the influence of fake tracks on jet reconstruction using Unified Flow Objects as inputs to the anti-k$_T$ algorithm for the ATLAS Collaboration. High-p$_T$ and dense environments can lead to degraded performance in reconstruction, resulting in an increase in f...
| Autores principales: | , |
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| Lenguaje: | eng |
| Publicado: |
2021
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| Materias: | |
| Acceso en línea: | http://cds.cern.ch/record/2779420 |