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- W4284673612 abstract "High-multiplicity signatures at particle colliders can arise in Standard Model processes and beyond. With such signatures, difficulties often arise from the large dimensionality of the kinematic space. For final states containing a single type of particle signature, this results in a combinatorial problem that hides underlying kinematic information. We explore using a neural network that includes a Lorentz Layer to extract high-dimensional correlations. We use the case of squark decays in $R$-Parity-violating Supersymmetry as a benchmark, comparing the performance to that of classical methods. With this approach, we demonstrate significant improvement over traditional methods." @default.
- W4284673612 created "2022-07-08" @default.
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- W4284673612 date "2022-07-05" @default.
- W4284673612 modified "2023-09-26" @default.
- W4284673612 title "Solving combinatorial problems at particle colliders using machine learning" @default.
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- W4284673612 doi "https://doi.org/10.1103/physrevd.106.016001" @default.
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