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Use of Machine Learning for Anomaly Detection Problem in Large Astronomical Databasesстатья
Информация о цитировании статьи получена из
Scopus
Дата последнего поиска статьи во внешних источниках: 27 сентября 2021 г.
- Авторы: Malanchev K., Volnova A., Kornilov M., Pruzhinskaya M., Ishida E., Mondon F., Korolev V.
- Сборник: Selected Papers of the XXI International Conference "Data Analytics and Management in Data Intensive Domains"
- Серия: CEUR Workshop Proceedings
- Том: 2523
- Год издания: 2019
- Первая страница: 205
- Последняя страница: 216
- Аннотация: http://ceur-ws.org/Vol-2523/https://dspace.kpfu.ru/xmlui/handle/net/151948In this work, we address the problem of anomaly detection in largeastronomical databases by machine learning methods. The importance of suchstudy is justified by the presence of a large amount of astronomical data thatcannot be processed only by human resource. We focus our attention on findinganomalous light curves in the Open Supernova Catalog. Few types of anomaliesare considered: the artifacts in the data, the cases of misclassification and thepresence of previously unclassified objects. On a dataset of ~ 2000 supernova(SN) candidates, we found several interesting anomalies: one active galacticnucleus (SN2006kg), one binary microlensing event (Gaia16aye),representatives of rare classes of SNe such as super-luminous supernovae, andhighly reddened objects.
- Добавил в систему: Пружинская Мария Викторовна