  In recent years, neural network-based classification has been used to improve
data analysis at collider experiments. While this strategy proves to be hugely
successful, the underlying models are not commonly shared with the public and
rely on experiment-internal data as well as full detector simulations. We show
a concrete implementation of a newly proposed strategy, so-called Classifier
Surrogates, to be trained inside the experiments, that only utilise publicly
accessible features and truth information. These surrogates approximate the
original classifier distribution, and can be shared with the public.
Subsequently, such a model can be evaluated by sampling the classification
output from high-level information without requiring a sophisticated detector
simulation. Technically, we show that Continuous Normalizing Flows are a
suitable generative architecture that can be efficiently trained to sample
classification results using Conditional Flow Matching. We further demonstrate
that these models can be easily extended by Bayesian uncertainties to indicate
their degree of validity when confronted with unknown inputs by the user. For a
concrete example of tagging jets from hadronically decaying top quarks, we
demonstrate the application of flows in combination with uncertainty estimation
through either inference of a mean-field Gaussian weight posterior, or Monte
Carlo sampling network weights.
