  We describe here an efficient machine-learning based approach for the
optimization of parameters used for extracting the arrival time of waveforms,
in particular those generated by the detection of 511 keV annihilation
gamma-rays by a 60 cm3 coaxial high purity germanium detector (HPGe). The
method utilizes a type of artificial neural network (ANN) called a
self-organizing map (SOM) to cluster the HPGe waveforms based on the shape of
their rising edges. The optimal timing parameters for HPGe waveforms belonging
to a particular cluster are found by minimizing the time difference between the
HPGe signal and a signal produced by a BaF2 scintillation detector. Applying
these variable timing parameters to the HPGe signals achieved a
gamma-coincidence timing resolution of ~ 4.3 ns at the 511 keV photo peak
(defined as 511 +- 50 keV) and a timing resolution of ~ 6.5 ns for the entire
gamma spectrum--without rejecting any valid pulses. This timing resolution
approaches the best obtained by analog nuclear electronics, without the
corresponding complexities of analog optimization procedures. We further
demonstrate the universality and efficacy of the machine learning approach by
applying the method to the generation of secondary electron time-of-flight
spectra following the implantation of energetic positrons on a sample.
