Ponente
Descripción
Imaging Atmospheric Cherenkov Telescopes (IACT) rely on the Electromagnetic Calorimetry technique to record gamma rays of cosmic origin. Therefore, they use combined analog and digital electronics for their trigger systems, implementing simple but fast algorithms. Such trigger techniques are forced by the extremely high data rates and strict timing requirements. In recent years, a design of an Advanced Camera as an upgrade for the Large-Sized Telescopes (LSTs) of the Cherenkov Telescope Array Observatory (CTAO) has been proposed. This camera will be based on Silicon PhotoMultipliers (SiPM) and a new fully digital trigger system incorporating Machine Learning algorithms. The critical improvement relies on implementing those algorithms in Field Programmable Gate Arrays (FPGAs), to increase the sensitivity and efficiency of real-time decision-making while fulfilling timing constraints. In addition, building on our prior experience in IACT event reconstruction using Deep Learning (DL), we are currently engaged in applying analogous algorithms to address the challenge of offline reducing the CTA data volume.
We are currently developing all the elements of an ML-based IACT trigger system, including a PCB prototype to test multi-gigabit optical transceivers and using development boards as an ML-algorithm testbench. Additionally, we also aim to integrate DL capabilities into the CTA offline analysis pipeline, seeking a more efficient processing chain in both computational and storage aspects.
