Anomaly detection in the Zwicky Transient Facility DR3. Issue 4 (6th February 2021)

• Record Type: Journal Article
• Authors: Malanchev, K L; Pruzhinskaya, M V; Korolev, V S; Aleo, P D; Kornilov, M V; Ishida, E E O; Krushinsky, V V; Mondon, F; Sreejith, S; Volnova, A A; Belinski, A A; Dodin, A V; Tatarnikov, A M; Zheltoukhov, S G
• Is Part Of: Monthly notices of the Royal Astronomical Society. Volume 502:Issue 4(2021)
• Page Start: 5147
• Page End: 5175
• Publication Date: 2021-02-06
• Subjects: methods: data analysis -- astronomical data bases: miscellaneous -- stars: variables: general; Astronomy -- Periodicals; Periodicals; 520.5
• DOI: 10.1093/mnras/stab316
• Languages: English
• ISSNs: 0035-8711

Abstract

ABSTRACT: We present results from applying the SNAD anomaly detection pipeline to the third public data release of the Zwicky Transient Facility (ZTF DR3). The pipeline is composed of three stages: feature extraction, search of outliers with machine learning algorithms, and anomaly identification with followup by human experts. Our analysis concentrates in three ZTF fields, comprising more than 2.25 million objects. A set of four automatic learning algorithms was used to identify 277 outliers, which were subsequently scrutinized by an expert. From these, 188 (68 per cent) were found to be bogus light curves – including effects from the image subtraction pipeline as well as overlapping between a star and a known asteroid, 66 (24 per cent) were previously reported sources whereas 23 (8 per cent) correspond to non-catalogued objects, with the two latter cases of potential scientific interest (e.g. one spectroscopically confirmed RS Canum Venaticorum star, four supernovae candidates, one red dwarf flare). Moreover, using results from the expert analysis, we were able to identify a simple bi-dimensional relation that can be used to aid filtering potentially bogus light curves in future studies. We provide a complete list of objects with potential scientific application so they can be further scrutinised by the community. These results confirm the importance of combining automatic machine learning algorithms with domain knowledge in the construction of recommendation systems for astronomy. Our code is publicly available. 1