Speaker
Description
The IceCube Neutrino Observatory issues a variety of real-time alerts that identify high-energy neutrino events with a high probability of astrophysical origin. These alerts rely on rapid reconstruction of the incident particle's direction and energy, enabling follow-up observations by other telescopes and observatories in the context of multi-messenger astronomy. Traditionally, reconstruction methods for these alerts have been likelihood-based, requiring assumptions about event morphology. Recent advances in machine learning, both broadly and within IceCube, now enable work towards a fast and flexible event reconstruction without the need for such priors. In this talk, I will present a proposed GraphNet-based neural network approach for fast reconstruction of high-energy neutrinos.
