ELEPHANT: ExtragaLactic alErt Pipeline for Hostless AstroNomical Transients

¹ The Oskar Klein Centre, Department of Astronomy, Stockholm University, AlbaNova 106 91, Stockholm, Sweden
² Independent Researcher, Ingolstadt, Germany
³ Instituto de Astronomia, Geofísica e Ciências Atmosféricas da USP, 05508-900 São Paulo, Brazil
⁴ Institute of Astronomy and Kavli Institute for Cosmology, Madingley Road, Cambridge CB3 0HA, UK
⁵ Astronomical Observatory, University of Warsaw, Al. Ujazdowskie 4, 00-478 Warszawa, Poland
⁶ Université Clermont Auvergne, CNRS/IN2P3, LPC, 63000 Clermont-Ferrand, France
⁷ CENTRA, Universidade de Lisboa, FCUL, Campo Grande, Edif. C8, 1749-016 Lisboa, Portugal
⁸ Donald Bren School of Information and Computer Sciences, University of California, Irvine, CA 92697, USA
⁹ Business School, University of Edinburgh, 29 Buccleuch Pl, Edinburgh EH8 9JS, UK
¹⁰ Centre for Statistics, University of Edinburgh, Peter Guthrie Tait Rd, Edinburgh, EH9 3FD, UK
¹¹ Centre for Astrophysics Research, University of Hertfordshire, College Lane, Hatfield AL10 9AB, UK
¹² Independent Researcher, Budapest, Hungary
¹³ School of Mathematical and Physical Sciences, Macquarie University, NSW 2109, Australia

^★ Corresponding author; priscila.pessi@astro.su.se

Received: 28 April 2024
Accepted: 9 September 2024

Abstract

Context. Transient astronomical events that exhibit no discernible association with a host galaxy are commonly referred to as hostless. These rare phenomena can offer unique insights into the properties and evolution of stars and galaxies. However, the sheer number of transients captured by contemporary high-cadence astronomical surveys renders the manual identification of all potential hostless transients impractical. Therefore, creating a systematic identification tool is crucial for studying these elusive events.

Aims. We present the ExtragaLactic alErt Pipeline for Hostless AstroNomical Transients (ELEPHANT), a framework for filtering hostless transients in astronomical data streams. It was designed to process alerts from the Zwicky Transient Facility (ZTF) presented in the Fink broker; however, its underlying concept can be applied to other data sources.

Methods. We used Fink to access all the ZTF alerts produced between January 2022 and December 2023, selecting alerts associated with extragalactic transients reported in SIMBAD or TNS, as well as those classified as supernovae (SNe) or kilonovae (KNe) by the machine learning (ML) classifiers within the broker. We then processed the associated stamps using a sequence of image analysis techniques to retrieve hostless candidates.

Results. We find that ≲2% of all analyzed transients are potentially hostless. Among them, only ~10% have a spectroscopic class reported on TNS, with type Ia SNe being the most common class, followed by superluminous SNe. In particular, among the host-less candidates retrieved by our pipeline, there is SN 2018ibb, which has been proposed to be a pair instability SN candidate, and SN 2022ann, one of only five known SNe Icn. When no class is reported on TNS, the dominant classes are quasi-stellar object (QSO) and SN candidates, with the former obtained from SIMBAD and the latter inferred using the Fink ML classifier.

Conclusions. ELEPHANT represents an effective strategy to filter extragalactic events within large and complex astronomical alert streams. There are many applications for which this pipeline will be useful, ranging from transient selection for follow-up to studies of transient environments. The results presented here demonstrate the feasibility of developing specially crafted pipelines that enable a variety of scientific studies based on large-scale surveys.