Jun 14 – 16, 2023
AlbaNova Main Building
Europe/Stockholm timezone

Deep Learning Based Event Reconstruction for the IceCube-Gen2 Radio Detector

Not scheduled
15m
Oskar Klein Auditorium FR4 (AlbaNova Main Building)

Oskar Klein Auditorium FR4

AlbaNova Main Building

Roslagstullsbacken 21, 114 21 Stockholm
Poster Sektionen för elementarpartikel och astropartikelfysik Sektionen för elementarpartikel och astropartikelfysik

Speaker

Nils Heyer (Uppsala University)

Description

The planned in-ice radio array of IceCube-Gen2 at the South Pole will provide unprecedented sensitivity to ultra-high-energy (UHE) neutrinos in the EeV range. The ability of the detector to measure the neutrino’s energy, direction, and flavor is of crucial importance. This contribution presents an end-to-end reconstruction of all these quantities for both detector components of the hybrid radio array ('shallow' and 'deep') using deep neural networks (DNNs). We are able to predict the neutrino direction precisely for all event topologies, including the electron neutrino charged-current ($ν_e$-CC) interactions, which are more complex due to the LPM effect. This highlights the advantages of DNNs for modeling the complex correlations in radio detector data, thereby enabling a measurement of the neutrino energy and direction. We discuss how we can use normalizing flows to predict the PDF for each individual event which allows modeling the complex non-Gaussian uncertainty contours of the reconstructed neutrino direction. Finally, we discuss how this work can be used to further optimize the detector layout to improve its reconstruction performance.

Primary author

Nils Heyer (Uppsala University)

Co-authors

Dr Christian Glaser (Uppsala University) Dr Thorsten Glüsenkamp (Uppsala University)

Presentation materials

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