Machine learning methods for track classification in the AT-TPC

Abstract

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. Currently, events of interest are selected via cuts in the track fitting stage of the analysis workflow. An explicit classification step to single out the desired reaction product would result in more accurate physics results as well as a faster analysis process. We tested binary and multi-class classification methods on data produced by the $^{46}\text{Ar}(p,p)$ experiment run at the NSCL in September 2015. We found that fine-tuning a pre-trained convolutional neural network produced the most successful classifier of proton scattering events in the experimental data, when trained on both experimental and simulated data. We present results from this investigation and conclude with recommendations for event classification in future experiments.

Publication
Machine learning methods for track classification in the AT-TPC
Ryan Strauss
Ryan Strauss
PhD Student

My research interests focus in artificial intelligence and machine learning.