A Neural-Network-defined Gaussian Mixture Model for particle identification applied to the LHCb fixed-target programme

Understand

Particle identification in large high-energy physics experiments typically relies on classifiers obtained by combining many experimental observables.

• Predicting the probability density function (pdf) of such classifiers in the multivariate space covering the relevant experimental features is usually challenging.
• The detailed simulation of the detector response from first principles cannot provide the reliability needed for the most precise physics measurements.
• Data-driven modelling is usually preferred, though sometimes limited by the available data size and different coverage of the feature space by the control channels.