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We describe a technique to learn the underlying structure of collider eventsdirectly from the data, without having a particular theoretical model in mind.It allows to infer aspects of the theoretical model that may have given rise tothis structure, and can be used to cluster or classify the events for analysispurposes. The unsupervised machine-learning technique is based on theprobabilistic (Bayesian) generative model of Latent Dirichlet Allocation. Wepair the model with an approximate inference algorithm called VariationalInference, which we then use to extract the latent probability distributionsdescribing the learned underlying structure of collider events. We provide adetailed systematic study of the technique using two example scenarios to learnthe latent structure of di-jet event samples made up of QCD background eventsand either $t\bar{t}$ or hypothetical $W' \to (\phi \to WW) W$ signal events.

Learning the latent structure of collider events