Neutron Measurements and Reactor Antineutrino Search with the SNO+ Detector in the Water Phase

Abstract

The SNO+ experiment is a large-scale liquid scintillator neutrino experiment with a wide range of physics objectives. SNO+ has adopted a staged approach where the detector was first filled with ultra-pure water before substituting with liquid scintillator and the target isotope $^{130}$Te for neutrinoless double beta decay. During the SNO+ water phase, an $^{241}$Am$^{9}$Be source is deployed across the detector volume to calibrate the detector's energy response and its response to neutrons. The $^{241}$Am$^{9}$Be source emits a unique coincidence signal with the prompt event being a 4.4 MeV $\gamma$ and the delayed a neutron capture signal (2.2 MeV $\gamma$).

A novel, minimalistic, statistical analysis of the $^{241}$Am$^{9}$Be calibration data was designed and used to measure the capture time constant $\tau$, capture cross-section $\sigma_{H,t}$, and the neutron detection efficiency $E_{\textrm{center}}$ at the center of the detector: \begin{equation} \begin{aligned} &\tau = 202.35 \pm 0.42\ (stat.)\ ^{+0.38}_{-0.31}\ (syst.)~\mu\textrm{s}, \\ &\sigma_{H,t} = 336.3 ^{+1.2}_{-1.5}~\textrm{mb}, \\ &E_{\textrm{center}} = (50.8 \pm 0.6)\%. \end{aligned} \end{equation}

Additionally, with the help of Monte Carlo simulations, a volume-weighted neutron detection efficiency across the detector is evaluated to be $E_{\textrm{detector}} = (46.5 \pm 0.5\ (stat.\ only))\%$. The simulation is also central to an energy calibration using the 4.4 MeV $\gamma$ to measure the energy resolution and energy scale of the detector.

Finally, with $\sim$115 days of early water data, an upper limit, $\hat{\Phi}_{\bar{\nu}_e, \textrm{ult}}$ = $(1.76 \pm 0.29) \times 10^6 \bar{\nu}/(\textrm{cm}^2\cdot\textrm{s})$, on the reactor antineutrino flux for SNO+ is obtained using a maximum likelihood approach. The limit is about a factor of 9 higher than the expected signal in SNO+, which can be calculated using available reactor output power data.