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T: Fachverband Teilchenphysik

T 29: Deep Learning II

T 29.1: Vortrag

Dienstag, 26. März 2019, 16:00–16:15, H06

Reconstruction of Muons with Recurrent Neural Networks for the IceCube Experiment — •Gerrit Wrede, Gisela Anton, and Thorsten Glüsenkamp for the IceCube collaboration — Erlangen Centre for Astroparticle Physics, Erlangen, Germany

The IceCube neutrino observatory is searching for point sources in the astrophysical neutrino flux. Relativistic muons created by muon neutrinos offer a good angular resolution and are thus an ideal channel for the detection of point sources. Recurrent neural networks are a class of artificial neural networks designed to handle time series data, such as the signatures created by muons traveling through the IceCube detector. In this talk, I will present an approach to use recurrent neural networks for muon reconstruction in IceCube.