Speaker
Description
We build a simple probabilistic model for collider events represented by a pattern of points in a space of high-level observables. The model is based on three assumptions for the point data: the measurements in individual events are discrete, exchangeable, and generated from a mixture of latent distributions, or 'themes'. The result is a mixed-membership model known as Latent Dirichlet Allocation (LDA), extensively used in natural language processing, biology and many unsupervised machine learning applications. By training on point patterns in the Lund jet plane, we demonstrate that a two-theme LDA model can be used for fully unsupervised event classification. As an example, we show that the LDA classifier can detect a BSM heavy resonance hidden in dijet data.
| Affiliation | Zurich U. |
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