Jul 12 – 23, 2021
Europe/Berlin timezone

Neutrino direction and flavor-id reconstruction from radio detector data using deep learning

Jul 14, 2021, 12:00 PM
1h 30m


Poster NU | Neutrinos & Muons Discussion


Mr Sigfrid Stjärnholm (Uppsala University, Sweden)


With the construction of RNO-G and plans for IceCube-Gen2, neutrino astronomy at EeV energies is at the horizon for the next years. Here, we determine the neutrino pointing capabilities and explore the sensitivity to the neutrino flavor for an array of shallow radio detector stations. The usage of deep learning for event reconstruction is enabled through recent advances in simulation codes that allow the simulation of realistic training data sets. A large data set of expected radio signals for a broad range of neutrino energies between 100 PeV and 10 EeV is simulated using NuRadioMC. A deep neural network is trained on this low-level data and we find a direction resolution of a few degrees for all triggered events. We present the model architecture, how we optimized the model, and how robust the model is against systematic uncertainties. Furthermore, we explore the capabilities of a radio neutrino detector to determine the flavor id.


deep learning; Askaryan; UHE neutrinos; in-ice radio detection; direction reconstruction; radio

Subcategory Experimental Methods & Instrumentation

Primary authors

Mr Sigfrid Stjärnholm (Uppsala University, Sweden) Mr Oscar Ericsson (Uppsala University, Sweden) Christian Glaser (Uppsala University, Sweden)

Presentation materials