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A search has been made for neutrinos from the hep reaction in the Sun and from the diffuse supernova neutrino background (DSNB) using data collected during the first operational phase of the Sudbury Neutrino Observatory, with an exposure of 0.65 ktons yr. For the hep neutrino search, two events are observed in the effective electron energy range of 14:3 MeV(More)
Results are reported from a joint analysis of Phase I and Phase II data from the Sudbury Neutrino Observatory. The effective electron kinetic energy threshold used is T eff = 3.5 MeV, the lowest analysis threshold yet achieved with water Cherenkov detector data. In units of 10 6 cm −2 s −1 , the total flux of active-flavor neutrinos from 8 B decay in the(More)
A search has been made for sinusoidal periodic variations in the 8 B solar neutrino flux using data collected by the Sudbury Neutrino Observatory over a 4-year time interval. The variation at a period of 1 yr is consistent with modulation of the 8 B neutrino flux by the Earth's orbital eccentricity. No significant sinusoidal periodicities are found with(More)
We report results from a combined analysis of solar neutrino data from all phases of the Sudbury Neutrino Observatory. By exploiting particle identification information obtained from the proportional counters installed during the third phase, this analysis improved background rejection in that phase of the experiment. The combined analysis resulted in a(More)
Results are reported on the measurement of the atmospheric neutrino-induced muon flux at a depth of 2 kilometers below the Earth's surface from 1229 days of operation of the Sudbury Neutrino Observatory (SNO). By measuring the flux of through-going muons as a function of zenith angle, the SNO experiment can distinguish between the oscillated and(More)
The Sudbury Neutrino Observatory (SNO) used an array of 3 He proportional counters to measure the rate of neutral-current interactions in heavy water and precisely determined the total active (x) 8 B solar neutrino flux. This technique is independent of previous methods employed by SNO.
We present a novel algorithm for optimizing the order in which Chinese characters are learned, one that incorporates the benefits of learning them in order of usage frequency and in order of their hierarchal structural relationships. We show that our work outperforms previously published orders and algorithms. Our algorithm is applicable to any scheduling(More)