A comparison study of GEANT4 and MCNP6 on neutron-induced gamma simulation

Ge, Yi; Liang, Jingang; Zhang, Qiong; Tang, W.; Munoz-Garcia, Agustin

doi:10.1016/j.apradiso.2022.110514

Cited by 6 publications

(1 citation statement)

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“…At present, a cross-software comparison is critically important to increase confidence in simulation predictions, in particular for the predictions of neutron transport, interaction, and capture. These types of studies have been successfully performed before [11][12][13], but the comparisons are highly application-specific and a rigorous treatment will be tailored to the question at hand. It is also highly beneficial to envision alternative designs that can significantly eliminate in-situ cosmogenic backgrounds without requiring large modifications to the baseline design.…”

Section: Motivationmentioning

confidence: 99%

Examining LEGEND-1000 cosmogenic neutron backgrounds in Geant4 and MCNP

Barton,

Xu,

Massarczyk

et al. 2024

J. Inst.

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For next-generation neutrinoless double beta decay experiments, extremely low backgrounds are necessary. An understanding of in-situ cosmogenic backgrounds is critical to the design effort. In-situ cosmogenic backgrounds impose a depth requirement and especially impact the choice of host laboratory. Often, simulations are used to understand background effects, and these simulations can have large uncertainties. One way to characterize the systematic uncertainties is to compare unalike simulation programs. In this paper, a suite of neutron simulations with identical geometries and starting parameters have been performed with Geant4 and MCNP, using geometries relevant to the LEGEND-1000 experiment. This study is an important step in gauging the uncertainties of simulations-based estimates. To reduce project risks associated with simulation uncertainties, a novel alternative shield of methane-doped liquid argon is considered in this paper for LEGEND-1000, which could achieve large background reduction without requiring significant modification to the baseline design.

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Section: Motivationmentioning

confidence: 99%