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

## TL;DR

This paper introduces a new neural network method to improve neutron multiplicity measurements by correcting for dead-time and background effects more accurately than previous methods.

## Contribution

A novel neural network-based approach for dead-time correction and background subtraction in neutron multiplicity measurements is proposed and validated.

## Key findings

- The neural network method achieved fractional errors smaller than 3% for neutron multiplicities around the peak of 252Cf.
- Existing methods showed larger uncertainties or systematic trade-offs in dead-time correction.
- The neural network approach can be easily expanded for higher neutron multiplicities.

## Abstract

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 \documentclass[12pt]{minimal}
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\begin{document}$$^{252}$$\end{document}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 correction of neutron multiplicity probabilities more accurately than the other methods with fractional errors smaller than 3% for multiplicities around the peak of \documentclass[12pt]{minimal}
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\begin{document}$$^{252}$$\end{document}252Cf. 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 be easily expanded if multiplicities greater than 10 are expected.