2017
DOI: 10.1016/j.astropartphys.2017.04.004
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Seasonal modulation of the 7 Be solar neutrino rate in Borexino

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Cited by 28 publications
(25 citation statements)
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“…In order to properly reproduce the spatial dependence of the energy response, events are simulated in the detector following their expected spatial distribution: while the ν and most of background events are expected to be uniformly distributed in the detector, 210 Po decays are simulated according to their actual spatial and time distribution obtained from experimental data. Note that data events due to the α decay of 210 Po are efficiently identified by tagging 210 Po with a pulse-shape discrimination method based on the multilayer perceptron (MLP) algorithm [23] (a particular class of neural network algorithms). Similarly, γs from external background are generated on the SSS and PMTs surfaces so that the radial distribution of the interactions inside the scintillator volume shows a clear decrease from the outer region of the detector toward the center.…”
Section: A the Monte Carlo Methodsmentioning
confidence: 99%
“…In order to properly reproduce the spatial dependence of the energy response, events are simulated in the detector following their expected spatial distribution: while the ν and most of background events are expected to be uniformly distributed in the detector, 210 Po decays are simulated according to their actual spatial and time distribution obtained from experimental data. Note that data events due to the α decay of 210 Po are efficiently identified by tagging 210 Po with a pulse-shape discrimination method based on the multilayer perceptron (MLP) algorithm [23] (a particular class of neural network algorithms). Similarly, γs from external background are generated on the SSS and PMTs surfaces so that the radial distribution of the interactions inside the scintillator volume shows a clear decrease from the outer region of the detector toward the center.…”
Section: A the Monte Carlo Methodsmentioning
confidence: 99%
“…The multilayer perceptron (MLP) is a nonlinear technique developed using deep learning for supervising binary classifiers, i.e., functions that can decide whether an input (represented by a vector of numbers) belongs to one class or another. In Borexino this technique was applied for α=β discrimination [83], and uses several pulseshape variables, parametrizing the event hit-time profile, as input. Among these variables are, for example, tail-tototal ratio for different time bins t n , mean time of the hits in the cluster, their variance, skewness, kurtosis, and so on.…”
Section: Multilayer Perceptronmentioning
confidence: 99%
“…• evidence that the 7 Be neutrino interaction rate displayed a seasonal modulation consistent with the varying solid angle between the earth and the sun [5];…”
Section: 4mentioning
confidence: 96%