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T: Fachverband Teilchenphysik

T 45: Neutrino Physics III

T 45.9: Talk

Wednesday, March 18, 2026, 18:15–18:30, AudiMax

Towards a High-Rate Active Neutrino Detector at FASER: Performance Studies with Deep Learning — Florian Bernlochner, Tobias Boeckh, Dhruv Chouhan, Jörn Mahlstedt, •Felix Antonio Junjiro Obando Molina, Matthias Schott, and Konstantinos Spyrou — Universität Bonn, Regina-Pacis-Weg 3, D-53113 Bonn, Germany

The FASER experiment at the LHC is a forward detector designed to study light, weakly interacting particles and has successfully established a dedicated neutrino program to measure high-energy collider neutrinos. During LHC Run-4, however, the expected increase in muon background rates will exceed the tolerable limits of the current emulsion-based neutrino detector, motivating the exploration of alternative technologies. We investigate the feasibility of an active neutrino detector concept based on multilayer active pixel sensors capable of operating at high rates. In this talk, we present a detailed study of neutrino reconstruction performance in such a detector using modern deep learning approaches, including deep neural networks (DNNs) and convolutional neural networks (CNNs), with a focus on efficient neutrino event classification in a challenging high-background environment.

Keywords: Neural Networks; FASERnu; Neutrino Event Classification; Silicon Detector