In the search for new physics at the Large Hadron Collider (LHC) a possible approach is to employ anomaly detection techniques to spot events that deviate from the standard model in an unsupervised manner. There have been many such studies using e.g. convolutional autoencoders. In previous GSoC projects, the usage of graph-based models has been very successful in generative tasks.Motivated by the success of graph-based models in various computational tasks, this project seeks to leverage non-local graph neural networks (GNNs) for the classification of jets in particle physics. Unlike conventional methods, which treat jets as independent entities, the proposed approach capitalizes on the inherent relational structure among particles within a jet, represented as a graph. The challenge is to account the long-range dependencies inherent to the jets, which regular GNNs fail to do.
Total project length: 175/350 hours.
Please use this link to access the test for this project.
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