Monte Carlo studies of scintillator efficiencies and scatter corrections for (n,2n) cross section measurements

Parker, J.B.; Fieldhouse, P.; Harrison, L.M.; Mather, D.S.

doi:10.1016/0029-554x(68)90083-9

Cited by 12 publications

(2 citation statements)

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“…Therefore, it is important to understand this process and the equation describing the time distribution. To be absorbed by a gadolinium loaded liquid scintillator the neutron needs to be moderated to a thermal energy (G) typically on the order of 0.05 to 0.1 eV 12 . The probability that a neutron moderated to an energy G becomes absorbed in the time range (t ′ , t ′ +dt ′ ) is given by equation ( 1), where t ′ is the time measured from the trigger reference point and dt ′ is an infinitesimal increment of time.…”

Section: Time Distribution Of Neutrons Captured In Gadolinium-loaded ...mentioning

confidence: 99%

“…The time distribution of neutron capture in liquid scintillators is a crucial part of the analytical methods for dead-time correction. Parker 12 explored in depth the parameters affecting the time distribution of neutron capture events in liquid scintillators for (n, 2n) cross section measurements. A different formulation of the analytical method was introduced by Spencer 13 where the background and dead-time corrections are performed separately.…”

Section: Introductionmentioning

confidence: 99%

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Innovative dead-time correction and background subtraction for neutron multiplicity measurements using neural networks

Garcia-Duarte,

Harke,

Mishnayot

et al. 2023

Preprint

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The number of neutrons emitted from a nuclear reaction plays a crucial role in various fields, including nuclear theory, nuclear nonproliferation, nuclear energy and nuclear criticality safety. Accurate determination of neutron multiplicities requires the application of several corrections, with dead-time correction and background subtraction being particularly significant. These corrections become more challenging for neutron detectors with time-dependent neutron capture. In this work, we perform a comprehensive study of three existing methods used for dead-time correction and background subtraction in neutron detectors with time-dependent neutron capture. The methods were tested for dead-times in the range from 0 to 1 μs using a Monte Carlo model simulating the dead-time and background effects in the standard neutron multiplicity probability distribution of 252Cf. The previous methods showed larger than desired uncertainty or systematic trade off. Those uncertainties prompted the development of a novel approach using neural networks trained with data from Monte Carlo simulations. The Neural Network method enabled the extraction of neutron multiplicity probabilities with accuracy ten times higher than the other methods with errors smaller than 1%. A similar approach using neural networks could be applied to problems where the system being studied can be accurately simulated without having an accurate analytical description available. The neural network method presented in this paper can easily be expanded if multiplicities are greater than 10.

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Section: Time Distribution Of Neutrons Captured In Gadolinium-loaded ...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%