Speaker
Description
SiPMs (Silicon Photomultipliers) have recently been studied as candidates for building photon cameras in RICH (Ring Imaging Cherenkov) detectors. SiPM-based cameras improve detection efficiency (up to $60\%$), spatial resolution (mm), timing ($< 100$\, ps), scalability, and magnetic field immunity. Nevertheless, for single-photon detection, SiPM thermal noise ($\sim 10^2$\,kHz/mm$^2$) and photon-background sources (scintillation or scattering) pose severe challenges for free-streaming readout systems. In this work, we study the viability of an ML (machine learning) trigger implemented in the CBM (Compressed Baryonic Matter) RICH front-end electronics (edge computing). The ML algorithm aims to filter out fake events caused by the SiPM thermal noise while keeping Cherenkov ring events.