Szczegóły publikacji
Opis bibliograficzny
Towards detection of anomalous cosmic ray signals for observations acquired from Cosmic Ray Extremely Distributed Observatory mobile detectors / Tomasz HACHAJ, Łukasz BIBRZYCKI, Marcin PIEKARCZYK, Olaf Bar, Michał Niedźwiecki, Sławomir Stuglik, Piotr Homola, Dmitriy Beznosko, David Alvarez-Castillo, Bożena Poncyljusz, Ophir Ruimi, Oleksandr Sushchov, Krzysztof RZECKI // Engineering Applications of Artificial Intelligence ; ISSN 0952-1976. — 2025 — vol. 161 pt. B art. no. 112109, s. 1–13. — Bibliogr. s. 12–13, Abstr. — Publikacja dostępna online od: 2025-09-09
Autorzy (13)
- AGHHachaj Tomasz
- AGHBibrzycki Łukasz
- AGHPiekarczyk Marcin
- Bar Olaf
- Niedźwiecki Michał
- Stuglik Sławomir
- Homola Piotr
- Beznosko Dmitriy
- Alvarez-Castillo David E.
- Poncyljusz Bożena
- Ruimi Ophir
- Sushchov Oleksandr
- AGHRzecki Krzysztof
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 162463 |
|---|---|
| Data dodania do BaDAP | 2025-09-22 |
| Tekst źródłowy | URL |
| DOI | 10.1016/j.engappai.2025.112109 |
| Rok publikacji | 2025 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Czasopismo/seria | Engineering Applications of Artificial Intelligence |
Abstract
In this work, we propose and test a method that allows the detection of anomalous cosmic ray signals acquired using Complementary Metal-Oxide-Semiconductor detectors. The method uses unsupervised embedding based on Principal Component Analysis which we named Eigenhits in apparent analogy to Eigenfaces. The embedding generated using Eigenhits allows the detection of potential anomalies, defined as images whose position described by the embedding relative to a given measure is above a certain distance threshold from other images. Thus, the problem of anomaly detection was reduced to the problem of detecting outliers which can be solved, for example, using clustering algorithms. We conducted tests of our approach on the Cosmic Ray Extremely Distributed Observatory (CREDO) dataset containing 13168 images and obtained satisfactory results demonstrating the stability and effectiveness of the method. The embedding generation method we propose in this paper and the evaluation of its effectiveness in detecting anomalies in images of cosmic ray events from CREDO dataset is a pioneering study with many critical applications.
