par Abbasi, Rasha;Aguilar Sanchez, Juan Antonio 
;Chau, Thien Nhan ;Maris, Ioana Codrina ;Schlüter, Felix ;Toscano, Simona ; [et al.]
Référence (26 July 2023 through 3 August 2023: Nagoya), 38th International Cosmic Ray Conference, ICRC 2023, Pos proceedings of science (444), 1102
Publication Publié, 2024-03-01
;Chau, Thien Nhan ;Maris, Ioana Codrina ;Schlüter, Felix ;Toscano, Simona ; [et al.]Référence (26 July 2023 through 3 August 2023: Nagoya), 38th International Cosmic Ray Conference, ICRC 2023, Pos proceedings of science (444), 1102
Publication Publié, 2024-03-01
Publication dans des actes
| Résumé : | 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 and direction is of crucial importance. This contribution presents an end-to-end reconstruction of both of 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’s direction and energy precisely for all event topologies, including the electron neutrino charged-current (νe-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. |
