This Letter reports the first scientific results from the observation of antineutrinos emitted by fission products of ^{235}U at the High Flux Isotope Reactor. PROSPECT, the Precision Reactor ...Oscillation and Spectrum Experiment, consists of a segmented 4 ton ^{6}Li-doped liquid scintillator detector covering a baseline range of 7-9 m from the reactor and operating under less than 1 m water equivalent overburden. Data collected during 33 live days of reactor operation at a nominal power of 85 MW yield a detection of 25 461±283 (stat) inverse beta decays. Observation of reactor antineutrinos can be achieved in PROSPECT at 5σ statistical significance within 2 h of on-surface reactor-on data taking. A reactor model independent analysis of the inverse beta decay prompt energy spectrum as a function of baseline constrains significant portions of the previously allowed sterile neutrino oscillation parameter space at 95% confidence level and disfavors the best fit of the reactor antineutrino anomaly at 2.2σ confidence level.
We present a detailed report on sterile neutrino oscillation and 235Uν¯e energy spectrum measurement results from the PROSPECT experiment at the highly enriched High Flux Isotope Reactor (HFIR) at ...Oak Ridge National Laboratory. In 96 calendar days of data taken at an average baseline distance of 7.9 m from the center of the 85 MW HFIR core, the PROSPECT detector has observed more than 50,000 interactions of νe produced in beta decays of 235U fission products. New limits on the oscillation of ν¯e to light sterile neutrinos have been set by comparing the detected energy spectra of ten reactor-detector baselines between 6.7 and 9.2 meters. Measured differences in energy spectra between baselines show no statistically significant indication of ν¯e to sterile neutrino oscillation and disfavor the reactor antineutrino anomaly best-fit point at the 2.5σ confidence level. The reported 235U ν¯e energy spectrum measurement shows excellent agreement with energy spectrum models generated via conversion of the measured 235U beta spectrum, with a χ2/d.o.f. of 31/31. PROSPECT is able to disfavor at 2.4σ confidence level the hypothesis that 235U ν¯e are solely responsible for spectrum discrepancies between model and data obtained at commercial reactor cores. A data-model deviation in PROSPECT similar to that observed by commercial core experiments is preferred with respect to no observed deviation, at a 2.2σ confidence level.
This Letter reports the first measurement of the 235U $\bar{ν}$e energy spectrum by PROSPECT, the Precision Reactor Oscillation and Spectrum experiment, operating 7.9 m from the 85 MWth highly ...enriched uranium (HEU) High Flux Isotope Reactor. With a surface-based, segmented detector, PROSPECT has observed 31678±304(stat) $\bar{ν}$e-induced inverse beta decays, the largest sample from HEU fission to date, 99% of which are attributed to 235U. Despite broad agreement, comparison of the Huber 235U model to the measured spectrum produces a χ2/ndf=51.4/31, driven primarily by deviations in two localized energy regions. The measured 235U spectrum shape is consistent with a deviation relative to prediction equal in size to that observed at low-enriched uranium power reactors in the $\bar{ν}$e energy region of 5–7 MeV.
Abstract The objective of the cyclotron radiation emission spectroscopy (CRES) technology is to build precise particle energy spectra. This is achieved by identifying the start frequencies of charged ...particle trajectories which, when exposed to an external magnetic field, leave semi-linear profiles (called tracks) in the time–frequency plane. Due to the need for excellent instrumental energy resolution in application, highly efficient and accurate track reconstruction methods are desired. Deep learning convolutional neural networks (CNNs) - particularly suited to deal with information-sparse data and which offer precise foreground localization—may be utilized to extract track properties from measured CRES signals (called events) with relative computational ease. In this work, we develop a novel machine learning based model which operates a CNN and a support vector machine in tandem to perform this reconstruction. A primary application of our method is shown on simulated CRES signals which mimic those of the Project 8 experiment—a novel effort to extract the unknown absolute neutrino mass value from a precise measurement of tritium β − -decay energy spectrum. When compared to a point-clustering based technique used as a baseline, we show a relative gain of 24.1% in event reconstruction efficiency and comparable performance in accuracy of track parameter reconstruction.
PDF
