Orateur
Description
Within the CTAO collaboration, GammaLearn is a project to develop deep learning solutions for the event reconstruction of Imaging Atmospheric Cherenkov Telescopes directly from the acquired images. Previous work demonstrated very good performances of the developed architecture network ganma-PhysNet on simulated and real data in constrained conditions. However, image acquisition covers heterogeneous observation conditions that are not explicitly included in the learning process and from which the model does not benefit to improve performances. How to take these additional variables into account during the training and inference processes?
We tested two different architectures and compare their performances: a Vanilla architecture, without conditional variables, and an architecture implementing Conditional Batch Norm (CBN) with NSB and/or pointing direction as conditioning variables. We used data covering the sky from 6deg to 30deg zenith and on top of which we simulated NSB as added noise.
Our results show that the CBN architecture does not significantly improve energy and angular resolutions while increasing variability, thus reducing model robustness to different acquisition conditions. Furthermore, it brings more complexity in the architecture and in the training and inference phases. Our study concludes that in our case study, a simpler architecture with enough training data is a preferable solution.
