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A simulation model is developed to train Artificial Neural Networks (ANN),for precise timing of PICOSEC Micromegas detector signals. The aim is todevelop fast, online timing algorithms as well as minimising the information tobe saved during data acquisition. PICOSEC waveforms were collected anddigitised by a fast oscilloscope during a femptosecond-laser test beam run. Adata set comprising waveforms collected with attenuated laser beam intensity,eradicating the emission of more than one photoelectron per light pulse fromthe PICOSEC photocathode, was utilised by a simulation algorithm to generatewaveforms to train an ANN. A second data set of multi-photoelectron waveformswas used to evaluate the ANN performance in determining the PICOSEC SignalArrival Time, relative to a fast photodiode time-reference. The ANN timingperformance is the same as the results of a full offline signal processing,achieving a timing precision of 18.3$\pm$0.6 ps.

Timing techniques with picosecond-order accuracy for novel gaseous detectors