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2021

Bayesian Probabilistic Modelling for Four-Tops at the LHC

Alvarez, Ezequiel, Dillon, Barry M., Faroughy, Darius A. et al.

Understand

Monte Carlo (MC) generators are crucial for analyzing data in particle collider experiments.

However, often even a small mismatch between the MC simulations and the measurements can undermine the interpretation of the results.
This is particularly important in the context of LHC searches for rare physics processes within and beyond the standard model (SM).
One of the ultimate rare processes in the SM currently being explored at the LHC, $pp\to t\bar tt \bar t$ with its large multi-dimensional phase-space is an ideal testing ground to explore new ways to reduce the impact of potential MC mismodelling on experimental results.