This is the third workshop dedicated to GraphNeT – Graph neural networks for neutrino telescope event reconstruction. The goal is to bring together researchers working at the intersection of neutrino telescope experiments and machine learning to meet likeminded researchers, discuss the latest progress, and develop new solutions to physics challenges using graph neural networks (GNNs) — putting graphs to work.
The workshop, and particularly the two half-day hackathons, are focussed on putting the GraphNeT framework into use for physics — in IceCube and other experiments. In this way, we hope to foster collaboration on common tools in order to advance physics research faster than individual experiments can on their own.
The workshop is aimed at active practitioners who are eager to learn about GNNs, to showcase their existing physics work leveraging GNNs, and to participate in active, hands-on problem solving using GNNs. Participants who may not have substantial experience with GNNs, or machine learning (ML) in general, but who are interested in leveraging these techniques in their physics analysis are encouraged to join. As are ML experts, keen on exploring the newest technical developments within the field and help ensure their impact on neutrino physics.
While the workshop's presentations can feature most work at the intersection of ML and neutrino telescopes, hackathon participants ought to have an interest in trying out the GraphNeT code — either because they’re open to using it as their main framework, or because they want to see how other people are working with GNNs. We will have some experiment-independent MC data, so people should be able to show up with nothing more than their laptop and still be able to do some actual work/experimentation.
The workshop will be held on scenic Bornholm, a Danish island in the Baltic Sea. Specifically, the workshop activities will take place at Strandhotellet, a hotel and conference venue at the island's northernmost tip.
There may be some limitations to the workshop's capacity, in which case preference will be given to attendees who are actively working at the intersection of machine learning and neutrino telescope physics.
The organisers would like to acknowledge the generous support for this workshop, provided by the Danish Data Science Academy.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 890778.