Signal processing and machine learning algorithms for precise timing with PICOSEC Micromegas detectors

Abstract High particle rates in current and future experiments make pile-up phenomena a critical issue for extracting useful information. In this context, timing serves as a crucial fourth dimension for triggering and event reconstruction. The PICOSEC Micromegas detector has demonstrated precise timing capabilities on the order of tens of picoseconds. In this work, we develop and evaluate novel signal-processing algorithms to demonstrate the detector's potential for online precise timing. In particular, we propose an algorithm based on Artificial Neural Networks (ANNs), trained using a physically motivated model. The performance of the different algorithms is assessed using experimental data recorded during laser beam tests at the IRAMIS Facility of CEA Saclay. A timing resolution of 18.3 ± 0.6 ps, corresponding to 7.8 photoelectrons in the gas volume, is achieved — comparable to results obtained with the standard Constant Fraction Discrimination (CFD) technique. Additionally, we present an alternative algorithm that uses the integrated charge of the pulse exceeding a defined threshold as a parameter to correct for systematic effects.