Machine learning algorithms for classifying multi-neutron decay measurements of neutron-unbound systems

The MoNA Collaboration studies neutron-unbound systems using a set of large-area high-efficiency neutron detectors, the Modular Neutron Array (MoNA) and the Large multi-Institutional Scintillator Array (LISA). These detectors enable invariant mass spectroscopy experiments to study neutron-unbound nuclei and provide information for benchmarking models of the atomic nucleus. A crucial step in the analysis of systems that decay by emitting multiple neutrons involves classifying events according to the number of neutrons detected. To address this, machine learning techniques are being tested as a means to improve the efficiency of the classification process. We will present preliminary results from training a neural network to classify simulated two-neutron events and discuss plans to test this network with labeled data.