https://doi.org/10.1140/epjp/s13360-024-05620-2
Regular Article
Application of machine learning methods for detecting atypical structures in astronomical maps
Bauman Moscow State Technical University, 105005, Moscow, Russian Federation
Received:
3
July
2024
Accepted:
4
September
2024
Published online:
21
November
2024
The paper explores the use of various machine learning methods to search for heterogeneous or atypical structures on astronomical maps. The study was conducted on the maps of the cosmic microwave background radiation from the Planck mission obtained at various frequencies. The algorithm used found a number of atypical anomalous structures in the actual maps of the Planck mission. This paper details the machine learning model used and the algorithm for detecting anomalous structures. A map of the position of such objects has been compiled. The results were compared with known astrophysical processes or objects. Future research involves expanding the dataset and applying various algorithms to improve the detection and classification of outliers.
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© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
