  Background modelling is one of the main challenges in particle physics data
analysis. Commonly employed strategies include the use of simulated events of
the background processes, and the fitting of parametric background models to
the observed data. However, reliable simulations are not always available or
may be extremely costly to produce. As a result, in many cases, uncertainties
associated with the accuracy or sample size of the simulation are the limiting
factor in the analysis sensitivity. At the same time, parametric models are
limited by the a priori unknown functional form and parameter values of the
background distribution. These issues become ever more pressing when large
datasets become available, as it is already the case at the CERN Large Hadron
Collider, and when studying exclusive signatures involving hadronic
backgrounds.
  Two novel and widely applicable non-parametric data-driven background
modelling techniques are presented, which address these issues for a broad
class of searches and measurements. The first, relying on ancestral sampling,
uses data from a relaxed event selection to estimate a graph of conditional
probability density functions of the variables used in the analysis, accounting
for significant correlations. A background model is then generated by sampling
events from this graph, before the full event selection is applied. In the
second, a generative adversarial network is trained to estimate the joint
probability density function of the variables used in the analysis. The
training is performed on a relaxed event selection which excludes the signal
region, and the network is conditioned on a blinding variable. Subsequently,
the conditional probability density function is interpolated into the signal
region to model the background. The application of each method on a benchmark
analysis is presented in detail, and the performance is discussed.
