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May 8 – 12, 2023
Norfolk Waterside Marriott
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HyperTrack: neural combinatorics for high energy physics

May 9, 2023, 11:30 AM
Hampton Roads VII (Norfolk Waterside Marriott)

Hampton Roads VII

Norfolk Waterside Marriott

235 East Main Street Norfolk, VA 23510
Oral Track 9 - Artificial Intelligence and Machine Learning Track 9 - Artificial Intelligence and Machine Learning


Mieskolainen, Mikael (Imperial College London)


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

Primary author

Mieskolainen, Mikael (Imperial College London)

Presentation materials