The project is to implement certain graph neural networks that are invariant to different symmetry groups, relevant to physics, and test them for jet tagging, which is a classification task in particle physics. The network will be analysed, and the number of FLOPS, tentative mathematical guarantees and comparison with current best models will be determined (LorentzNet, ParticleTransformer, PELICAN). Furthermore following Walters and Wang Approximately Equivariant Networks for Imperfectly Symmetric Dynamics the network equivariance is mitigated to better fit the real world data produced in the CMS detector in CERN. Particle physics is an ideal playground for testing equivariant networks as the Standard Model is full of symmetries. The input data can consist of the transversal momentum and two angles of a constituent particle. Therefore implementing networks is not a simple application of already existing architectures as the equivariance should exist for each input specifically and not globally on the entries. Giving the network only to symmetry equivariant functions to learn should theoretically induce better performance, it could be more understandable in terms of mathematical analysis and should be more efficient for the inference. The need for low latency models in particle physics is particularly important for the selection of stored events in the CMS Experiment, therefore developing a solution for jet tagging could potentially help to construct an algorithm that would only register interesting events during a collision. It is a nice illustration of the usage of approximately equivariant networks in a real case scenario. Co-supervised with Prof. Siddhartha Mishra (Professor of Applied Mathematics, ETH).

Get inspiration from the work here and implement one of these algorithms for real data taking in CMS. Co-supervised with Sioni Summers (CERN). Details TBA.