End-to-end multi-particle reconstruction in high occupancy imaging calorimeters with graph neural networks

End-to-end multi-particle reconstruction in high occupancy imaging calorimeters with graph neural networks

Abstract We present an end-to-end reconstruction algorithm to build particle candidates from detector hits in next-generation granular calorimeters similar to that foreseen for the high-luminosity upgrade of the CMS detector. The algorithm exploits a distance-weighted graph neural net...

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Bibliographic Details
Main Authors: Qasim, Shah R., Chernyavskaya, Nadezda, Kieseler, Jan, Long, Kenneth, Viazlo, Oleksandr, Pierini, Maurizio, Nawaz, Raheel
Other Authors: Massachusetts Institute of Technology. Department of Physics
Format: Article
Language:English
Published: Springer Berlin Heidelberg 2022
Online Access:https://hdl.handle.net/1721.1/145257

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https://hdl.handle.net/1721.1/145257

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