Background modelling is one of the main challenges of particle physics analyses at hadron colliders. Commonly employed strategies are the use of simulations based on Monte Carlo event generators or the use of parametric methods. However, sufficiently accurate simulations are not always available or may be computationally costly to produce in high statistics, leading to uncertainties that can limit the sensitivity of searches. On the other hand, parametric methods rely on the use of a functional form with free parameters to fit the observed data, which may bias the extraction of a potential signal.
A novel approach for non-parametric data-driven background modelling is presented, relying on a relaxed version of the event selection to estimate conditional probability density functions. Two different techniques are used for its implementation. The first is based on ancestral sampling while the second relies on a generative adversarial network. The application of each implementation of the method on a case study is presented, and their performance is discussed.
Meeting ID: 959 2359 8030
Meeting passcode: 405246
