2nd GSI/FAIR AI Workshop

Artificial intelligence methods developed for one scientific domain often struggle to generalize to other fields that produce irregular and high dimensional detector data, creating a need for domain agnostic reconstruction approaches.
BRAID is a research consortium developing machine learning models particularly graph neural network and attention-based architectures, that can work across this kind of irregular, high-dimensional data from experiments in high energy physics, astroparticle physics, hadron and nuclear physics.
Here, we give an overview of this recently started research initiative, with a focus on its first use cases in experiments in hadron physics at GSI/FAIR.