Speaker
Santiago Tanco
Description
Topic modeling techniques allow to learn robust representations of a data in terms of latent themes or topics. In this talk, I will present an application of Latent Dirichlet Allocation that exploits information from multiple Monte Carlo simulation setups of known processes to learn the shapes of observable distributions directly from data. This approach provides a general framework to infer the latent probabilistic structure underlying the observed data, which permits flexible and data-driven background inference and uncertainty estimation. I will demonstrate this method in a specific application to multijet final states at collider experiments.