Study of electron reconstruction with deep learning in the SND@LHC experiment

Date

2026-6-30

Author

Türk, Berk

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SND@LHC is designed to perform measurements with high-energy (100 GeV − 1 TeV ) neutrinos produced at the LHC in an unexplored pseudo-rapidity region of 7.2 < η < 8.6. The detector design is well suited to identify all flavours of neutrinos’s interactions and measure their properties. Therefore, SND@LHC has a unique opportunity to probe physics of heavy flavour production in a region of phase space previously unexplored by the other LHC experiments. This region is of particular interest also for future circular colliders and for predictions of very high-energy atmospheric neutrinos. In addition, it also allows efficiently searching for Feebly Interacting Particles via its scattering in the detector target. Electron identification in the SciFi system is crucial for many physics studies. In this thesis, electrons produced in neutrino interactions will be reconstructed using deep learning methods.

Subject Keywords

SND@LHC, Neutrino, Deep Learning, Energy Reconstruction, Electron Identification

URI

https://hdl.handle.net/11511/119782

Collections

Graduate School of Natural and Applied Sciences, Thesis