Neutron Tagging following Atmospheric Neutrino Events in a Water Cherenkov Detector

TL;DR

This paper develops a neural network-based neutron-tagging method for atmospheric neutrino events in Super-Kamiokande IV, achieving a 26% detection efficiency and analyzing extensive data to measure neutron capture lifetime.

Contribution

It introduces a novel neural network approach for neutron tagging in water Cherenkov detectors and provides detailed efficiency and lifetime measurements.

Findings

Neutron tagging efficiency estimated at 26%.

Mis-tag rate of 0.016 per neutrino event.

Neutron capture lifetime measured as 218 ± 9 μs.

Abstract

We present the development of neutron-tagging techniques in Super-Kamiokande IV using a neural network analysis. The detection efficiency of neutron capture on hydrogen is estimated to be 26%, with a mis-tag rate of 0.016 per neutrino event. The uncertainty of the tagging efficiency is estimated to be 9.0%. Measurement of the tagging efficiency with data from an Americium-Beryllium calibration agrees with this value within 10%. The tagging procedure was performed on 3,244.4 days of SK-IV atmospheric neutrino data, identifying 18,091 neutrons in 26,473 neutrino events. The fitted neutron capture lifetime was measured as 218 \pm 9 \mu s.