https://doi.org/10.1051/epjconf/202531913012
Transfer Learning in KM3NeT/ORCA for Neutrino Event Reconstruction
LPC CAEN, Normandie Univ, ENSICAEN, UNICAEN, CNRS/IN2P3, 6 boulevard Maréchal Juin, Caen, 14050 France
* e-mail: mozun@lpccaen.in2p3.fr
Published online: 6 March 2025
This study explores using transformer models to analyze data from the KM3NeT/ORCA neutrino telescope. Due to the increasing detector’s size, reconstructing neutrino events is challenging. By training models on simulations for the full detector (115 detection units) and fine-tuning them on smaller configurations, significant performance improvements can be achieved compared to models trained from scratch on limited data samples. This approach also helps estimate the detector’s sensitivity as it grows.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
