Supernova search with active learning in ZTF DR3

¹ Université Clermont Auvergne, CNRS/IN2P3, LPC, 4 Avenue Blaise Pascal, Clermont-Ferrand 63000, France
e-mail: pruzhinskaya@gmail.com
² Lomonosov Moscow State University, Sternberg astronomical institute, Universitetsky pr. 13, Moscow 119234, Russia
³ Space Research Institute of the Russian Academy of Sciences (IKI), 84/32 Profsoyuznaya Street, Moscow 117997, Russia
⁴ Department of Astronomy, University of Illinois at Urbana-Champaign, 1002 West Green Street, Urbana, IL 61801, USA
⁵ National Research University Higher School of Economics, 21/4 Staraya Basmannaya Ulitsa, Moscow 105066, Russia
⁶ Center for AstroPhysical Surveys (CAPS), National Center for Supercomputing Applications, 1205 West Clark Street, Urbana, IL, 61801, USA
⁷ Independent researcher, Sovetskaya st. 6, 140185 Zhukovsky, Moscow region, Russia
⁸ Laboratory of Astrochemical Research, Ural Federal University, ul. Mira d. 19, Yekaterinburg 620002, Russia
⁹ Physics Department, Brookhaven National Laboratory, 98 Rochester St, Upton, NY 11973, USA

Received: 8 October 2022
Accepted: 22 February 2023

Abstract

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.