Speaker
Description
GraphNeT, a deep learning framework for neutrino telescopes, provides a common library for deep learning experts and neutrino physicists in need of advanced deep learning techniques for their analyses. GraphNeT is a detector agnostic framework, making it easy and intuitive to apply deep learning techniques from one experiment to another, and to export models to the physicists that relies on them. GraphNeT lowers technical barriers, enabling physicists to utilize deep learning without extensive expertise, and sets the stage for inter-experimental collaboration. This poster presentation highlights the key developments of GraphNeT 2.0 and their impact on how they provide a way for neutrino telescopes to work together on advancing the state-of-the-art for deep learning based techniques in the field.
