Some experimental determinations of the number of prompt neutrons from fission

Moat, A.; Mather, D.S.; McTaggart, M.H.

doi:10.1016/0368-3230(61)90046-5

Cited by 16 publications

(5 citation statements)

References 18 publications

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“…Therefore, the equations of Boldeman's analytical method exhibit degeneracy in the results for a dead-time of τ = 0.9 µs. This observation is consistent with Moat's findings 4 , where it was emphasized that Diven's analytical method, closely resembling Boldeman's, becomes impractical when dealing with dead-times larger than a few hundred nanoseconds. We conducted additional simulations varying the dead-time values in the range of 0 to 1 µs to identify the point at which Boldeman's method exhibits degeneracy.…”

Section: Boldeman's Analytical Methodssupporting

confidence: 90%

“…Upon scrutinizing Boldeman's analytical method 9 , limitations surfaced particularly from τ = 0.2 µs onward. Through extensive iterations, a significant degeneracy was revealed, rendering Boldeman's analytical method impractical for dead-times approaching a few hundred nanoseconds 4 . This degeneracy, vividly illustrated in Figure 3, led to substantial uncertainties from a dead-time of τ = 0.2 µs and higher, where absolute errors exceeded 0.4, resulting in more than 40% variation in neutron multiplicity probabilities from the true values.…”

Section: Resultsmentioning

confidence: 99%

“…Moat's method 4 involves obtaining the coefficients of the matrix T that transforms D = {D 0 , ..., D l } into F ′ = {F ′ 0 , ..., F ′ l } through a MC simulation. The coefficients T mn of the transformation matrix are defined as…”

Section: Moat's MC Methodsmentioning

confidence: 99%

“…The larger the concentrations of neutron capture element loaded in the scintillator, the shorter the time window where neutrons are absorbed, increasing the overlap probability. Moat 4 conducted a similar experiment with a dead-time of τ = 0.25 µs using their own simulation method to overcome the limitations of Diven's analytical method. In a second paper by the same group, Mather 5 clarified that a Monte Carlo (MC) model was used to obtain a matrix that transforms the experimental into the real neutron multiplicity probability distribution.…”

Section: Introductionmentioning

confidence: 99%

“…As anticipated, the analytical method proposed by Diven 2 is inadequate when dealing with a dead-time of τ = 0.9 µs. However, Dushin 15 successfully employed the MC method introduced by Moat 4 to perform the dead-time correction for τ = 0.3 µs.…”

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: Boldeman's Analytical Methodssupporting

confidence: 90%

Section: Resultsmentioning

confidence: 99%

Section: Moat's MC Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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