Speaker
Enhao Song
(University of Virginia)
Description
The NOνA experiment is studying a variety of neutrino properties using a long-baseline neutrino beam and near and far detectors. Due to its surface proximity and large area the NOνA far detector is particularly sensitive to a large range of magnetic monopole masses and energies. The monopole trigger, like all NOvA data-driven triggers, is software only using data collected and sent to a farm of on-site computers. The trigger must be fast, have a high efficiency, and a large rejection factor in order to reduce the background of over 150,000 cosmic rays that course through the detector every second to no more than 10 s-1. The present cut-based trigger has an efficiency uncertainty due to irreducible uncertainties in the expected monopole energy deposit in the detector. We discuss and show the performance of a novel neural net trigger with a much improved efficiency and lower uncertainties. Sensitivities for the monopole search using the previous trigger data and the improved trigger data will be given.
Primary author
Enhao Song
(University of Virginia)
Co-author
Prof.
E. Craig Dukes
(University of Virginia)