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Uncovering hidden new physics patterns at high-energy colliders with probabilistic models

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  • UserDarius Faroughy (UZH)
  • ClockThursday 25 February 2021, 16:00-17:00
  • HouseVirtual Seminar .

If you have a question about this talk, please contact Joseph Davighi .

The seminar will take place via Zoom here.

Abstract: Individual events at high-energy colliders like the LHC can be represented by a sequence of measurements, or ‘point patterns’ in a space of high-level observables. We build a simple generative probabilistic model for event patterns that can be used for unsupervised classification tasks in Beyond the SM studies. In order to arrive to this model we assume that event measurements are exchangeable, discrete, and generated from multiple latent distributions, called themes. The resulting probabilistic model is a mixed-membership model known as Latent Dirichlet Allocation (LDA), a model 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 discover heavy resonances hidden in dijet data in a fully unsupervised manner.

The slides are available here

This talk is part of the HEP phenomenology joint Cavendish-DAMTP seminar series.

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