Efficient Machine Learning Approach for Optimizing the Timing Resolution of a High Purity Germanium Detector
This work addresses timing optimization in nuclear physics instrumentation, offering a digital alternative to complex analog methods, but it is incremental as it applies existing neural network techniques to a specific domain.
The researchers tackled the problem of optimizing timing resolution for a high purity germanium detector using a machine learning approach, achieving a gamma-coincidence timing resolution of ~4.3 ns at the 511 keV photo peak and ~6.5 ns for the entire spectrum without pulse rejection.
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.