Solid-State Nuclear Track Detector Analysis Without Thresholding

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Solution Overview

Problem

Existing methods for analyzing solid-state nuclear track detectors (SSNTDs) face challenges in reliably determining particle information due to user bias introduced by thresholding techniques, which affect the accuracy of pit parameters and subsequent particle identification.

Innovation Solution

A method that analyzes SSNTD images by obtaining pit occurrence and first pit information, calculating the distribution of pit information, comparing it to an expected distribution based on calibration information, and determining particle information without the need for thresholding, thereby reducing user bias.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If thresholding techniques are used to analyze SSNTD images, then pit parameters can be extracted, but user bias is introduced affecting measurement precision

Engineering Contradiction:
Improvepit parameter accuracyVSAvoidobjectivity of analysis
Core Design Contradiction:
Measurement precisionVSReliability

Solution Approach 1:

The patent extracts and removes the thresholding step from the image analysis process. By eliminating this subjective parameter selection, the method avoids user bias while still enabling pit parameter extraction through alternative objective means such as automated edge detection and Hough transform algorithms that do not require manual threshold setting.

Inventive Principle:
Principle #2Taking out (Extraction)

Solution Approach 2:

The analysis system performs self-calibration and automatic parameter determination without requiring user intervention for threshold selection. The system uses built-in algorithms to automatically identify pit boundaries and extract parameters, making the process objective and reproducible across different users and conditions.

Inventive Principle:
Principle #25Self-service

2Difficulty of detecting and measuring

If thresholding is applied to SSNTD images, then pit detection is enabled, but the results are affected by subjective user inputs

Engineering Contradiction:
Improvepit detection capabilityVSAvoidsubjectivity of user input
Core Design Contradiction:
Difficulty of detecting and measuringVSEase of operation

Solution Approach 1:

The patent replaces the manual mechanical process of threshold selection with automated computational algorithms. Instead of requiring users to manually set thresholds, the system uses digital image processing techniques including edge detection, Hough transform, and automated parameter extraction to objectively identify and measure pits.

Inventive Principle:
Principle #28Mechanics substitution (Replace mechanical system)

3Measurement precision

If calibration is performed with multiple particle species, then particle identification accuracy is improved, but device complexity increases

Engineering Contradiction:
Improveparticle species identification accuracyVSAvoidcalibration process complexity
Core Design Contradiction:
Measurement precisionVSDevice complexity

Solution Approach 1:

The patent performs calibration measurements with multiple particle species in advance, creating a database of reference responses. During actual analysis, the system automatically compares measured pit parameters against this pre-established calibration database to identify particle species, thereby achieving high accuracy without requiring complex real-time multi-species calibration procedures.

Inventive Principle:
Principle #10Preliminary action

Applied Scientific Principles

This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.

Function Achieved in This Case

This approach enables more reliable determination of particle information, improving the unambiguous detection of particles and reducing the influence of subjective user inputs, thus enhancing the accuracy of particle species and energy spectrum analysis.

Implementation Method 1

A particle of a certain energy impinges on the SSNTD. As this particle moves throughout the detector, it deposits its energy and create a damage path within the SSNTD plate.

Methodology Applied
Scientific EffectEnergy deposition:

Implementation Method 2

The damage path, or damage paths, is also referred to in the art as latent track and is the result, for instance, of the destruction of chemical bonds within the SSNTD plate.

Methodology Applied
Scientific EffectLatent track formation:

Implementation Method 3

The latent track SSNTD is usually etched. Etching entails immersing the SSNTD into a suitable chemical solution to open the latent track and make it observable by an optical microscope.

Methodology Applied
Scientific EffectChemical etching:

Implementation Method 4

The pit is the physical damage created on the SSNTD plate.

Methodology Applied
Scientific EffectPit formation:

Implementation Method 5

The pits are illuminated by a uniform light source that results in dark circles or ellipses.

Methodology Applied
Scientific EffectOptical illumination: Light

Data Source

PatentEP4506734A1Method for analyzing a solid-state nuclear track detector
Publication Date: 2025.02.12 MARVEL FUSION GMBH
  • EP4506734A1 patent drawingFigure 1
  • EP4506734A1 patent drawingFigure 2A
  • EP4506734A1 patent drawingFigure 2B

AI summary

The present disclosure pertains to a method for analyzing a solid-state nuclear track detector, SSNTD, image including pits, to determine particle information for at least one particle species. The method comprises the following steps of: A first step of obtaining a SSNTD image including pits. A second step of obtaining pit occurrence information from the SSNTD image, the pit occurrence information being indicative of a number of pits in the SSNTD image. A third step of obtaining first pit information from the SSNTD image, the first pit information being indicative of a physical property of the pits. A fourth step of obtaining a distribution of the pit information based on the first pit information and the pit occurrence information. A fifth step of calculating an expected distribution of the first pit information for the at least one particle species based on calibration information. A sixth step of comparing the expected distribution to the distribution of the pit information. A seventh step of determining particle information for the at least one particle species based on the result of the comparison, the particle information being indicative of a physical property of the at least one particle species, and the calibration information being indicative of a relationship between a second pit information and the at least one particle species at given analysis conditions.