Testing neural networks for classifying multi-neutron decay measurements of neutron-unbound systems

ORAL

Abstract

The MoNA Collaboration investigates 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). Together with the Sweeper magnet and its ancillary detectors, MoNA-LISA enables invariant mass spectroscopy experiments to study neutron-unbound nuclei around and beyond the dripline thus providing information to benchmark nuclear structure models. 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, small neural networks (< 100 nodes) are being tested as a means to improve the efficiency of the classification process. Preliminary results will be presented from tests with data from two previous MoNA-LISA experiments to measure two-neutron-unbound systems 26O and 10He.

*This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics, under Award Number DE-SC0022037.

Presenters

  • Thomas Redpath

    • Virginia State University

Authors

  • Thomas Redpath

    • Virginia State University
  • Jaylen I Rasberry

    • Virginia State University
  • Clifton D Kpadehyea

    • Virginia State University