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T: Fachverband Teilchenphysik

T 59: Neutrino Astronomy III

T 59.7: Vortrag

Mittwoch, 18. März 2026, 17:45–18:00, KS H C

Neural Network-based DAQ System for in-ice Radio Detection of Neutrinos for RNO-G and IceCube-Gen2 — •Adam Rifaie for the RNO-G collaboration — Bergische Universität Wuppertal, Wuppertal, Deutschland

Detecting astrophysical neutrinos at energies above 10 PeV is challenging due to their extremely low flux. The state-of-the-art detectors, such as RNO-G, and the planned IceCube-Gen2 Radio Array, exploit radio emissions via the Askaryan effect. The km-scale attenuation length of radio signals in ice enables large-scale detectors spanning several tens or hundreds of kilometres.

High trigger rates of such large-scale detectors demand efficient trigger systems and high data purity, such as Neural Network (NN)-based triggers. Previous simulation studies estimate an increase in the detection rates of astrophysical neutrinos by up to a factor of 2 at energies of 10  PeV, doubling the effective detection volume of the detector for no additional costs.

This presentation briefly describes the NNs we will be testing. Followed by the first lab measurements using the new DAQ system, NuRadioDAQ, and compares them to projected estimates based on simulated data.

Keywords: RNO-G; IceCube-Gen2; Neural Network; Trigger; DAQ