Speaker
Description
Deep learning methods have emerged as a powerful alternative to reconstruct physical properties directly from images of atmospheric events acquired by Imaging Atmospheric Cherenkov Telescopes (IACTs). In the context of the Cherenkov Telescope Array Observatory (CTAO) first Large-Sized Telescope (LST-1), the specialized architecture gamma-PhysNet has demonstrated strong performances on simulated and real data in constrained conditions. However, image acquisition often involves heterogeneous observation conditions not explicitly incorporated into the learning process, limiting potential performance gains.
In order to take these additional variables into account, we tested and compared two multi-modal architectures against the vanilla version of gamma-PhysNet: (1) a Conditional Batch Normalization (CBN) model using Night Sky Background (NSB) levels and telescope pointing directions as conditional inputs, and (2) an intermediate information fusion architecture with NSB and pointing directions as additional inputs.
Using simulated data with zenith angles from 6° to 30° and added NSB noise, we found that neither the CBN nor the fusion architecture significantly improved energy or angular resolution compared to gamma-PhysNet. Additionally, these approaches introduced unnecessary architectural and training complexity.
Our study suggests that in our case, a simpler architecture with enough training data is a preferable solution.