The Cherenkov Telescope Array Observatory (CTAO) is the next generation of ground-based observatories employing the imaging air Cherenkov technique for the study of very high energy gamma rays.
The software Gammalearn proposes to apply Deep Learning as a part of the CTAO data analysis to reconstruct event parameters directly from images captured by the telescopes with minimal pre-processing to maximize the information conserved.
In CTAO, the data analysis will involve a data volume reduction that will definitely remove pixels. This step is necessary for data transfer and storage but could also involve information loss that could be used by sensitive algorithms such as neural networks (NN).
In this work, we evaluate the performance of the gamma-PhysNet NN when applying different cleaning masks on the input images from simulated as well as real data from the first Large-Sized Telescope.
This study is critical to assess the impact of previous steps in the data processing, mainly motivated by data compression.