Research output: Contribution to journal › Article › peer-review
Research output: Contribution to journal › Article › peer-review
}
TY - JOUR
T1 - Supernova search with active learning in ZTF DR3
AU - Pruzhinskaya, M. V.
AU - Ishida, E. E. O.
AU - Novinskaya, A. K.
AU - Russeil, E.
AU - Volnova, A. A.
AU - Malanchev, K. l.
AU - Kornilov, M. V.
AU - Aleo, P. D.
AU - Korolev, V. S.
AU - Krushinsky, V. V.
AU - Sreejith, S.
AU - Gangler, E.
N1 - 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.
PY - 2023/4/1
Y1 - 2023/4/1
N2 - 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.
AB - 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.
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UR - https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=tsmetrics&SrcApp=tsm_test&DestApp=WOS_CPL&DestLinkType=FullRecord&KeyUT=000967616600006
U2 - 10.1051/0004-6361/202245172
DO - 10.1051/0004-6361/202245172
M3 - Article
VL - 672
JO - Astronomy and Astrophysics
JF - Astronomy and Astrophysics
SN - 0004-6361
M1 - A111
ER -
ID: 38474797