Enabling the discovery of fast transients

A kilonova science module for the Fink broker

¹ Université Paris Cité, CNRS, AstroParticule et Cosmologie, 10 rue Alice Domon et Léonie Duquet, 75013 Paris, France
e-mail: biswas@apc.in2p3.fr
² LPC, Université Clermont Auvergne, CNRS/IN2P3, 4 avenue Blaise Pascal, 63000 Clermont-Ferrand, France
e-mail: emille.ishida@clermont.in2p3.fr
³ Université Paris-Saclay, CNRS/IN2P3, IJCLab, 15 rue Georges Clemenceau, 91405 Orsay, France
⁴ Centre for Astrophysics and Supercomputing, Swinburne University of Technology, Mail Number H29, PO Box 218, 31122 Hawthorn, VIC, Australia
⁵ ARC Centre of Excellence for Gravitational Wave Discovery (OzGrav), Mail number H74, PO Box 218, 31122 Hawthorn, Victoria, Australia
⁶ Lomonosov Moscow State University, Sternberg Astronomical Institute, Universitetsky pr. 13, Moscow 119234, Russia
⁷ Centre for Astrophysics Research, University of Hertfordshire, College Lane, Hatfield, AL10 9AB, UK
⁸ Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology, 70 Vassar St, Cambridge, MA 02139, USA

Received: 31 October 2022
Accepted: 21 June 2023

Abstract

Context. Large-scale astronomical surveys such as the Zwicky Transient Facility (ZTF) opened a new window of opportunity in the search for rare astrophysical phenomena. Community brokers, such as FINK, have the task of identifying interesting candidates and redistributing them to the community. For the specific case of fast transients, this identification should be done early, based on a limited number of observed photometric epochs, thus allowing it to trigger further observations.

Aims. We describe the fast transient classification algorithm in the centre of the kilonova (KN) science module currently implemented in the FINK broker, and we report classification results based on simulated catalogues and real data from the ZTF alert stream.

Methods. We used noiseless, homogeneously sampled simulations to construct a basis of principal components. All light curves from more realistic ZTF simulations were written as a linear combination of this basis. The corresponding coefficients were used as features in training a random forest classifier. The same method was applied to two different datasets, illustrating possible representations of ZTF light curves. The latter aimed to simulate the data situation found within the ZTF alert stream.

Results. Classification based on simulations mimicking ZTF alerts resulted in 69.30% precision and 69.74% recall when applied to a simulated test sample, thus confirming the robustness of precision results when limited to 30 days of observations. Dwarf flares and point Type Ia supernovae were the most frequent contaminants. The final trained model was integrated into the FINK broker and has been distributing fast transients, tagged as KN_candidates, to the astronomical community, especially through the GRANDMA collaboration.

Conclusions. We show that features specifically designed to grasp different light-curve behaviours provide enough information to separate fast (KN-like) from slow (non-KN-like) evolving events. This module represents one crucial link in an intricate chain of infrastructure elements for multi-messenger astronomy, which is currently being put in place by the FINK broker team in preparation for the arrival of data from the Vera Rubin Observatory Legacy Survey of Space and Time.