📝 SummarXiv

Abstract

The AdvCam is a next-generation camera for the Large-Sized Telescopes of the Cherenkov Telescope Array Observatory, based on silicon photomultipliers. Its fully digital readout system enables the design of new, sophisticated trigger logic. The Large-Sized Telescopes aim to cover the low-energy range of the cosmic gamma-ray spectrum, with a threshold starting at about 20 GeV, using the existing photomultiplier tube camera. The AdvCam, along with the new trigger logic, as shown by simulations, lowers the detectable energy threshold to 13 GeV. The proposed trigger logic has a multilevel structure. The first level involves fast coincidences among small pixel regions at a rate of approximately 1 GHz, while the second level processes all camera pixels within an approximately 10-nanosecond time window. Different families of machine learning algorithms optimized for FPGAs form the second-level trigger. In this work, we consider two main approaches: Deep Neural Networks and Density-Based Spatial Clustering of Applications with Noise, both running with latencies below 1 microsecond at a 1 MHz rate. This work provides a detailed description of the trigger chain and its performance, as studied through simulation.