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Machine learning based event reconstruction for the MUonE experiment

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arxiv 2402.02913 v2 pith:TDUTY6AD submitted 2024-02-05 hep-ex physics.ins-det

Milosz Zdybal , Marcin Kucharczyk , Marcin Wolter This is my paper · ORCID
classification hep-exphysics.ins-det
keywords reconstructionclassicalexperimentlearningmachinemuoneadvantageousalgorithm
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A proof-of-concept solution based on the machine learning techniques has been implemented and tested within the MUonE experiment designed to search for New Physics in the sector of anomalous magnetic moment of a muon. The results of the DNN based algorithm are comparable to the classical reconstruction, reducing enormously the execution time for the pattern recognition phase. The present implementation meets the conditions of classical reconstruction, providing an advantageous basis for further studies.

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  1. Polish national input to the 2026 update of the European Strategy for Particle Physics

    hep-ex 2025-04 unverdicted

    Poland recommends FCC-ee as the preferred next CERN collider and gives a linear e+e- collider second priority in its national input to the 2026 European Strategy update.

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