Meta-learning for multiple detector geometry modeling

The simulation of the passage of particles through the detectors of High Energy Physics (HEP) experiments is a core component of any physics analysis. A detailed and accurate simulation of the detector response using the Geant4 toolkit is a time and CPU consuming process. With the upcoming high luminosity LHC upgrade, with more complex events and a much increased trigger rate, the amount of required simulated events will increase. Several research directions investigated the use of Machine Learning based models to accelerate particular detector response simulation. This results in a specifically tuned simulation and generally these models require a large amount of data for training. Meta learning has emerged recently as fast learning algorithm using small training datasets. In this paper, we propose a meta-learning model that “learns to learn” to generate electromagnetic showers using a first-order gradient based algorithm. This model is trained on multiple detector geometries and can rapidly adapt to a new geometry using few training samples.