Cyclotron radiation emission spectroscopy signal classification with machine learning in project 8

Cyclotron radiation emission spectroscopy signal classification with machine learning in project 8
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项目 8 中使用机器学习的回旋辐射发射光谱信号分类

DOI:
10.1088/1367-2630/ab71bd
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发表时间:
2020
影响因子:
3.3
通讯作者:
Guigue, M
Guigue, M
中科院分区:
物理与天体物理2区
文献类型:
--
作者:
Esfahani, A Ashtari;Böser, S;Buzinsky, N;Cervantes, R;Claessens, C;Viveiros, L de;Fertl, M;Formaggio, J A;Gladstone, L;Guigue, M

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由Project 8开创的回旋辐射发射光谱(克雷斯)技术测量来自背景磁场中旋转的单个电子的电磁辐射,以构建用于β衰变研究和其他应用的高精度能谱。探测器、磁阱几何形状和电子动力学产生了大量复杂的电子信号结构,这些结构携带着关于区分物理特征的信息。利用机器学习模型,我们开发了一种基于这些特征的方案来分析和分类克雷斯信号。正确理解和利用这些特性将有助于改善回旋加速器频率重建,并提高项目8的潜力,以实现未来世界领先的氚端点测量灵敏度。
The cyclotron radiation emission spectroscopy (CRES) technique pioneered by Project 8 measures electromagnetic radiation from individual electrons gyrating in a background magnetic field to construct a highly precise energy spectrum for beta decay studies and other applications. The detector, magnetic trap geometry and electron dynamics give rise to a multitude of complex electron signal structures which carry information about distinguishing physical traits. With machine learning models, we develop a scheme based on these traits to analyze and classify CRES signals. Proper understanding and use of these traits will be instrumental to improve cyclotron frequency reconstruction and boost the potential of Project 8 to achieve world-leading sensitivity on the tritium endpoint measurement in the future.
DOI: 10.1007/bf01294491
发表时间: 1992
期刊: Zeitschrift für Physik A Hadrons and Nuclei
影响因子: --
作者:
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DOI: 10.1103/physrevc.99.055501
发表时间: 2019
期刊: Physical Review C
影响因子: 3.1
作者:
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