Validating Position Reconstruction Algorithm with \({}^{\mathbf{241}}\)Am–\({}^{\mathbf{9}}\)Be Neutron Source in DEAP-3600

IF 0.4 4区物理与天体物理 Q4 PHYSICS, NUCLEAR

Physics of Atomic Nuclei Pub Date : 2025-07-11 DOI:10.1134/S1063778825700280

A. V. Grobov, A. I. Ilyasov

引用次数: 0

Abstract

The DEAP-3600 experiment uses a modern coordinate reconstruction algorithm that utilizes machine learning. This algorithm performed well compared to likelihood-based approaches. Here we validate our neural network based algorithm on data obtained using \({}^{241}\)Am–\({}^{9}\)Be radioactive source. The results obtained confirm algorithm validity.

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在DEAP-3600中验证\({}^{\mathbf{241}}\) Am - \({}^{\mathbf{9}}\) Be中子源位置重建算法

DEAP-3600实验使用了一种利用机器学习的现代坐标重建算法。与基于似然的方法相比，该算法表现良好。在此，我们用\({}^{241}\) Am - \({}^{9}\) Be放射源获得的数据验证了基于神经网络的算法。结果验证了算法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Physics of Atomic Nuclei 物理-物理：核物理

CiteScore

0.60

自引率

25.00%

发文量

审稿时长

3-6 weeks

期刊介绍： Physics of Atomic Nuclei is a journal that covers experimental and theoretical studies of nuclear physics: nuclear structure, spectra, and properties; radiation, fission, and nuclear reactions induced by photons, leptons, hadrons, and nuclei; fundamental interactions and symmetries; hadrons (with light, strange, charm, and bottom quarks); particle collisions at high and superhigh energies; gauge and unified quantum field theories, quark models, supersymmetry and supergravity, astrophysics and cosmology.