The Majorana Demonstrator is a neutrinoless double-beta decay
experiment using high purity p-type point contact germanium detectors.
The waveforms produced by these detectors have subtle variation indicating the detailed energy and drift path information for each event. In
addition, the waveforms depend sensitively on crystal impurity levels, temperature, and operating voltage. We have developed a machine learning
algorithm which, given a set of calibration waveforms, can infer detector
parameters. Once these parameters are known, the high precision detector model can be used to fit the drift paths of individual waveforms. This
method can be used as a sensitive background rejection technique for the
Demonstrator or the proposed future LEGEND experiment. In order to
reach specific physics goals, the design of readout instrumentation must
be considered. The development of data acquisition technology for the
Demonstrator and future experiments is discussed.
|Institute||University of North Carolina Chapel Hill|
|Speaker||Samuel J. Meijer|