Bayesian Inference for Four Tops at the LHC

Abstract: Four top production is one of the last benchmarks of the SM explored at the LHC, and thus the intersection of state of the art experimental techniques and theoretical calculations. In this talk, we give a brief review of the main problems one faces when trying to disentangle signal from background in such a complex final state. We then propose a relatively simple probabilistic mixture model where the Monte Carlo simulations play the role of prior knowledge. Using a simulated dataset with "bad" priors and known numerical inference techniques, we are able to correct the initial modelling on the data to a certain degree. This in turn opens the door for a reduction of simulation systematics and a higher sensitivity to possible BSM effects.

high energy physics

Audience: researchers in the topic

IFT-Madrid Webinars