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. 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.

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• Unsupervised Learning for Identifying Events in Active Target Experiments
• Digital Signal Analysis based on Convolutional Neural Networks for Active Target Time Projection Chambers
• Time Projection Chamber for GADGET II
• Fine-tuning machine-learned particle-flow reconstruction for new detector geometries in future colliders
• Report from the A.I. For Nuclear Physics Workshop
• End-to-end analysis using image classification