Speaker
Description
The IceCube Neutrino Observatory, located at the geographic South Pole, has opened a new window into the universe through the detection of high-energy astrophysical neutrinos. This contribution presents recent advances in the application of machine learning (ML) techniques to key challenges in astroparticle physics using IceCube data. We highlight the role of deep learning in improving event reconstruction accuracy, real-time alert systems for multimessenger follow-up, the enhanced classification of neutrino events against atmospheric backgrounds, and the advancements in cosmic ray measurements. Techniques such as graph neural networks, convolutional architectures adapted to irregular detector geometries, recurrent neural networks, and uncertainty-aware models have demonstrated significant improvements in performance and interpretability. We further discuss recent scientific results made possible through these approaches. This contribution underscores the growing importance of AI in advancing the scientific reach of large-scale observatories in the future.
