Machine Learning Methods for Track Classification in the AT-TPC
This work addresses a domain-specific need for more efficient data analysis in nuclear physics experiments, representing an incremental application of existing methods to new data.
The study tackled the problem of automating event classification in the AT-TPC detector to improve accuracy and speed in physics analysis, finding that a Convolutional Neural Network was the most successful classifier for proton scattering events.
We evaluate machine learning methods for event classification in the Active-Target Time Projection Chamber detector at the National Superconducting Cyclotron Laboratory (NSCL) at Michigan State University. An automated method to single out the desired reaction product would result in more accurate physics results as well as a faster analysis process. Binary and multi-class classification methods were tested on data produced by the $^{46}$Ar(p,p) experiment run at the NSCL in September 2015. We found a Convolutional Neural Network to be the most successful classifier of proton scattering events for transfer learning. Results from this investigation and recommendations for event classification in future experiments are presented.