Detection of Potentially Anomalous Cosmic Particle Tracks Acquired with CMOS Sensors: Validation of Rough k–Means Clustering with PCA Feature Extraction

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I whakaputaina i:	International Journal of Applied Mathematics and Computer Science vol. 35, no. 1 (2025), p. 7
Kaituhi matua:	Hachaj, Tomasz
Ētahi atu kaituhi:	Piekarczyk, Marcin, Wąs, Jarosław
I whakaputaina:	De Gruyter Brill Sp. z o.o., Paradigm Publishing Services
Ngā marau:	Particle tracking Feature extraction Outliers (statistics) Data analysis Datasets Source code Cluster analysis Principal components analysis Sensors Clustering Cosmic rays CMOS Algorithms Image acquisition Image processing Vector quantization Image processing systems
Urunga tuihono:	Citation/Abstract Full Text - PDF
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Whakaahuatanga
Whakarāpopotonga:	We present a method capable of detecting potentially anomalous cosmic particle tracks acquired with complementary metal-oxide-semiconductor (CMOS) sensors. We apply a principal components analysis-based feature extraction method and rough k-means clustering for outlier detection. We evaluated our approach on more than 104 images acquired by the Cosmic Ray Extremely Distributed Observatory (CREDO). The method presented in this work proved to be an effective solution. The analysis of the behavior of the rough k-means clustering-based algorithm presented here and the method of selecting its parameters showed that the algorithm performs as expected and demonstrates efficiency, stability, and repeatability of results for the test data set. The results included in this work are very relevant to the international CREDO project and the broader problem of anomaly analysis in image data sets. We plan to deploy the presented methodology in the image processing pipeline of the large data set we are working on in the CREDO project. The results can be reproduced using our source code, which is published in an open repository.
ISSN:	1641-876X 2083-8492 0867-857X
DOI:	10.61822/amcs-2025-0001
Puna:	Advanced Technologies & Aerospace Database