Orateur
Description
Mismodeling of calorimeter shower shape observables has been present since the beginning of the ATLAS detector due to a mismodelling in the Geant4 detector simulation. Shower shape variables are discriminating observables used in the identification of electrons and photons, and accurate modelling of their distributions is essential for precision measurements and searches in high-energy physics. Traditionally, such discrepancies have been mitigated by procedures applying simple shifts and stretches to simulation, but these approaches neglect correlations between variables and are limited in accuracy.
A novel method that employs autoregressive normalizing flows to correct shower shapes in simulation to match data will be shown. Spline-based transforms parameterized by the outputs of an MPL are employed, providing flexible, high-dimensional density estimation while preserving correlations between observables. Results on the application of this method will be presented, demonstrating the potential of normalizing flows as a powerful tool for improving the agreement between simulation and experimental data. Such methods can be widely used in high-energy physics, where accurate modelling of complex multivariate distributions is required.
