I will introduce a new neural algorithm -- HyperTrack, designed for exponentially demanding combinatorial inverse problems of high energy physics final state reconstruction and high-level analysis at the LHC and beyond. Many of these problems can be formulated as clustering on a graph resulting in a hypergraph. The algorithm is based on a machine learned geometric-dynamical input graph constructor and a neural network operating on that graph. The neural model is built using a graph neural network and a set transformer, which are end-to-end optimized under a fusion loss function targeting simultaneously the graph node, edge and clustering objectives. The clustering procedure can be changed according to the problem complexity requirements, from a greedy diffusion like iteration to a more computationally demanding but powerful Monte Carlo search based. I will demonstrate the scalability and physics performance of this cutting-edge approach with simulations and discuss possible future directions towards a hybrid quantum computer algorithm.
|Consider for long presentation||Yes|