Speaker
Description
The ATLAS experiment at the Large Hadron Collider uses the Geant4 toolkit to simulate detailed Monte Carlo events spanning a broad range of physics processes. However, the full simulation is computationally expensive, with the main bottleneck originating from the modelling of particle showers in the calorimeter systems. To meet increasing demands, especially for the high-luminosity LHC era, ATLAS has deployed the AtlFast3 (AF3) suite as its current fast-simulation framework. A key component of AF3 is the modelling of Muon Punch-Through (MPT) particles: highly energetic hadrons that penetrate the calorimeters and produce activity in the muon spectrometer. The current MPT approach samples secondary particles via principal component analysis, with a neural network estimating punch-through probabilities. To handle the intricate correlation of the kinematic distributions and increased number of secondary species, a new punch-through model is now being developed. This contribution presents an overview of the existing MPT simulation and introduces the generative approach. The model is based on a transformer network to capture correlation of high dimensional input with a flow-matching method to fulfill the regression task for secondary particle kinematics. A Variational Bayesian layer is used as the linear layer to introduce uncertainty in the weights and model the stochasticity of the main kinematic distributions. The method is expected to improve accuracy and flexibility, with future plans to integrate it into the ATLAS software framework as part of AF3’s Geant4 fast-simulation model.