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A case study of sending graph neural networks back to the test bench for applications in high-energy particle physics
In high-energy particle collisions, the primary collision products usually decay further resulting in tree-like, hierarchical structures with a priori unknown multiplicity.
Optimal statistical inference in the presence of systematic uncertainties using neural network optimization based on binned Poisson likelihoods with nuisance parameters
Data analysis in science, e. g., high-energy particle physics, is often subject to an intractable likelihood if the observables and observations span a high-dimensional input space.
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