July 31, 2017 to August 4, 2017
Fermi National Accelerator Laboratory
US/Central timezone

A Neural Net Trigger for a Monopole Search with the NOvA Far Detector

Aug 3, 2017, 11:17 AM
1 West (Fermi National Accelerator Laboratory)

1 West

Fermi National Accelerator Laboratory

Presentation Beyond Standard Model Beyond Standard Model


Enhao Song (University of Virginia)


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)


Prof. E. Craig Dukes (University of Virginia)

Presentation materials