Energy Spectrum Counting Window Positioning for Liquid Scintillation
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Solution Overview
Problem
Existing methods for automatically locating the counting window in energy spectra, particularly for liquid scintillation counters, are inefficient and impractical due to the continuous nature of β-ray energy spectra and quenching effects, leading to non-normal distributions and the inability of peak-finding technologies to accurately determine the counting window.
Innovation Solution
A method involving energy spectrum partitioning into segments, calculation of a window locating index for each segment, and determination of a marked segment as the target counting window, utilizing algorithms to optimize the window location process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the method of traversing the figures of merit (FOM) of all possible counting windows is used, then the counting window can be located, but the time complexity is high and it is inefficient
Solution Approach 1:
The energy spectrum is divided into multiple segments, and the FOM calculation is performed separately for each segment. This segmentation reduces the computational burden compared to calculating FOM for all possible windows, while still identifying the optimal counting window location through segment-by-segment analysis.
Solution Approach 2:
The algorithm performs preliminary identification of candidate segments before finalizing the counting window location. By pre-identifying promising segments based on initial FOM calculations or other criteria, the method avoids exhaustive search of all possible windows, thereby reducing time complexity while maintaining accuracy.
2Extent of automation
If existing automatic peak-finding technology is used, then the process is automated, but it is not applicable due to continuous β-ray energy spectra and quenching effects causing non-normal distributions
Solution Approach 1:
The algorithm adapts to the specific characteristics of liquid scintillation spectra by modifying the FOM calculation parameters and criteria. Instead of assuming normal distributions as in conventional peak-finding methods, the algorithm uses parameters suitable for continuous, quenching-affected spectra, making automation applicable to this specific measurement type.
Solution Approach 2:
The method is designed to automatically identify counting windows in liquid scintillation spectra without requiring manual intervention or adjustment for quenching effects. The algorithm self-adapts to the spectral characteristics by using FOM calculations that are inherently suitable for continuous distributions, providing robust automatic locating capability.
Data Source
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AI summary
A method and an apparatus for locating an energy spectrum counting window, an electronic device and a storage medium are provided. The method includes: acquiring an energy spectrum to be processed, and partitioning the energy spectrum to be processed into at least two energy spectrum partitions according to channels, and determining at least one candidate energy spectrum segment according to the at least two energy spectrum partitions, where each candidate energy spectrum segment includes at least one energy spectrum partition; determining a window locating index of each candidate energy spectrum segment, and determining a marked energy spectrum segment from the at least one candidate energy spectrum segment according to the window locating index of each candidate energy spectrum segment; and determining a target counting window of the energy spectrum to be processed according to the marked energy spectrum segment.