Speaker
Description
Imaging Atmospheric Cherenkov Telescopes (IACT) 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, in the context of a new camera design for the Large-Sized Telescopes (LSTs) of the Cherenkov Telescope Array (CTA) based on Silicon PhotoMultipliers (SiPM), a new fully digital trigger system incorporating Artificial Intelligence (AI) algorithms is being developed. 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 AI-based IACT trigger system, including a PCB prototype to test multi-gigabit optical transceivers and using development boards as an AI-algorithm testbench. 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.
J.A. Barrio, A. Cerviño, J.L. Contreras, M. López, D. Martín, D. Nieto, A. Pérez, L.A. Tejedor
Grupo de Altas Energías, Instituto de Física de Partículas y del Cosmos, Universidad Complutense de Madrid
