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Updated: May 21, 2025

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基于深度学习的堆积校正算法用于高计数率测量下的光谱数据

Yiwei Huang1,2, Xiaoying Zheng1,2, Yongxin Zhu1,2

  • 1Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China.

Sensors (Basel, Switzerland)
|March 17, 2025
PubMed
概括

这项研究引入了一个深度学习框架来纠正聚效应在玛射线光谱学. 这种新的方法准确地回收能量光谱,改善同位素识别和活动估计在高计数率场景.

关键词:
深度学习是一种深度学习.高计数率 计数率很高.核光谱学 核光谱学脉冲堆积起来了.

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When magnetic nuclei in a sample achieve resonance and undergo relaxation, the signal detected in NMR is an approximately exponential free induction decay. Fourier transform of an exponential decay yields a Lorentzian peak in the frequency domain. Lorentzian peaks in an NMR spectrum are defined by their amplitude, full width at half maximum, and position, where the peak width is governed by the spin-spin relaxation time alone. In real experiments, however, the applied magnetic field is rendered...
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The probability of having two carbon-13 atoms next to each other is negligible because of the low natural abundance of carbon-13. Consequently, peak splitting due to carbon-carbon spin-spin coupling is not observed in spectra. However, protons up to three sigma bonds away split the carbon signal according to the n+1 rule, resulting in complicated spectra.
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Spin systems where the difference in chemical shifts of the coupled nuclei is greater than ten times J are called first-order spin systems. These nuclei are weakly coupled, and their chemical shifts and coupling constant can generally be estimated from the well-separated signals in the spectrum.
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