Supernova search with active learning in ZTF DR3
Электронный научный архив УРФУ
Информация об архиве | Просмотр оригинала| Поле | Значение | |
| Заглавие |
Supernova search with active learning in ZTF DR3
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| Автор |
Pruzhinskaya, M. V.
Ishida, E. E. O. Novinskaya, A. K. Russeil, E. Volnova, A. A. Malanchev, K. L. Kornilov, M. V. Aleo, P. D. Korolev, V. S. Krushinsky, V. V. Sreejith, S. Gangler, E. |
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| Тематика |
METHODS: DATA ANALYSIS
SUPERNOVAE: GENERAL SURVEYS GALAXIES LEARNING SYSTEMS PHOTOMETRY PIPELINES ACTIVE LEARNING ADAPTIVE LEARNING ASTRONOMICAL SURVEYS DATA SET GALACTIC LATITUDE LARGE-SCALES LEARNING TECHNIQUES METHODS. DATA ANALYSIS PHOTOMETRIC DATA SUPERNOVAE: GENERAL SUPERNOVAE |
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| Описание |
Context. We provide the first results from the complete SNAD adaptive learning pipeline in the context of a broad scope of data from large-scale astronomical surveys. Aims. The main goal of this work is to explore the potential of adaptive learning techniques in application to big data sets. Methods. Our SNAD team used Active Anomaly Discovery (AAD) as a tool to search for new supernova (SN) candidates in the photometric data from the first 9.4 months of the Zwicky Transient Facility (ZTF) survey, namely, between March 17 and December 31, 2018 (58 194 ≤ MJD ≤ 58 483). We analysed 70 ZTF fields at a high galactic latitude and visually inspected 2100 outliers. Results. This resulted in 104 SN-like objects being found, 57 of which were reported to the Transient Name Server for the first time and with 47 having previously been mentioned in other catalogues, either as SNe with known types or as SN candidates. We visually inspected the multi-colour light curves of the non-catalogued transients and performed fittings with different supernova models to assign it to a probable photometric class: Ia, Ib/c, IIP, IIL, or IIn. Moreover, we also identified unreported slow-evolving transients that are good superluminous SN candidates, along with a few other non-catalogued objects, such as red dwarf flares and active galactic nuclei. Conclusions. Beyond confirming the effectiveness of human-machine integration underlying the AAD strategy, our results shed light on potential leaks in currently available pipelines. These findings can help avoid similar losses in future large-scale astronomical surveys. Furthermore, the algorithm enables direct searches of any type of data and based on any definition of an anomaly set by the expert. © The Authors 2023.
Russian Foundation for Basic Research, РФФИ; Ministry of Education and Science of the Russian Federation, Minobrnauka: FEUZ-2020-0038; Centre National de la Recherche Scientifique, CNRS: 21-52-15024; Lomonosov Moscow State University, MSU We thank Anastasia Voloshina and Alexandra Zubareva for the assistance in variable star classification and analysis. We also thank Stephane Blodin and Alexandra Kozyreva for discussion involving PISN modelling. The reported study was funded by RFBR and CNRS according to the research project 𝒩o. 21-52-15024. We used the equipment funded by the Lomonosov Moscow State University Program of Development. The authors acknowledge the support by the Interdisciplinary Scientific and Educational School of Moscow University “Fundamental and Applied Space Research”. P.D.A. is supported by the Center for Astrophysical Surveys (CAPS) at the National Center for Supercomputing Applications (NCSA) as an Illinois Survey Science Graduate Fellow. V.V.K. is supported by the Ministry of science and higher education of Russian Federation, topic no. FEUZ-2020-0038. E.E.O.I. received financial support from CNRS International Emerging Actions under the project Real-time analysis of astronomical data for the Legacy Survey of Space and Time during 2021-2022. |
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| Дата |
2024-04-05T16:19:54Z
2024-04-05T16:19:54Z 2023 |
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| Тип |
Article
Journal article (info:eu-repo/semantics/article) |info:eu-repo/semantics/publishedVersion |
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| Идентификатор |
Pruzhinskaya, MV, Ishida, EEO, Novinskaya, AK, Russeil, E, Volnova, AA, Malanchev, KL, Kornilov, MV, Aleo, PD, Korolev, VS, Krushinsky, VV, Sreejith, S & Gangler, E 2023, 'Supernova search with active learning in ZTF DR3', Astronomy and Astrophysics, Том. 672, A111. https://doi.org/10.1051/0004-6361/202245172
Pruzhinskaya, M. V., Ishida, E. E. O., Novinskaya, A. K., Russeil, E., Volnova, A. A., Malanchev, K. L., Kornilov, M. V., Aleo, P. D., Korolev, V. S., Krushinsky, V. V., Sreejith, S., & Gangler, E. (2023). Supernova search with active learning in ZTF DR3. Astronomy and Astrophysics, 672, [A111]. https://doi.org/10.1051/0004-6361/202245172 0004-6361 Final All Open Access, Hybrid Gold, Green https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153368223&doi=10.1051%2f0004-6361%2f202245172&partnerID=40&md5=350b60a8a6014fd9884eb03cf432c500 https://www.aanda.org/articles/aa/pdf/2023/04/aa45172-22.pdf http://elar.urfu.ru/handle/10995/130408 10.1051/0004-6361/202245172 85153368223 000967616600006 |
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| Язык |
en
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| Права |
Open access (info:eu-repo/semantics/openAccess)
cc-by https://creativecommons.org/licenses/by/4.0/ |
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application/pdf
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| Издатель |
EDP Sciences
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| Источник |
Astronomy & Astrophysics
Astronomy and Astrophysics |
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