  Background reduction in the SuperCDMS dark matter experiment depends on
removing surface events within individual detectors by identifying the location
of each incident particle interaction. Position reconstruction is achieved by
combining pulse shape information over multiple phonon channels, a task
well-suited to machine learning techniques. Data from an Am-241 scan of a
SuperCDMS SNOLAB detector was used to study a selection of statistical
approaches, including linear regression, artificial neural networks, and
symbolic regression. Our results showed that simpler linear regression models
were better able than artificial neural networks to generalize on such a noisy
and minimal data set, but there are indications that certain architectures and
training configurations can counter overfitting tendencies. This study will be
repeated on a more complete SuperCDMS data set (in progress) to explore the
interplay between data quality and the application of neural networks.
