Enabling the discovery of fast transients: A kilonova science module for the Fink broker

Abstract

We describe the fast transient classification algorithm in the center of the kilonova (KN) science module currently implemented in the Fink broker and report classification results based on simulated catalogs and real data from the ZTF alert stream. We used noiseless, homogeneously sampled simulations to construct a basis of principal components (PCs). All light curves from a more realistic ZTF simulation 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 long (>30 days) and medium (<30 days) light curves. The latter aimed to simulate the data situation found within the ZTF alert stream. Classification based on long light curves achieved 73.87% precision and 82.19% recall. Medium baseline analysis resulted in 69.30% precision and 69.74% recall, thus confirming the robustness of precision results when limited to 30 days of observations. In both cases, 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. We showed that features specifically designed to grasp different light curve behaviors 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.

8 Pages, 12 Figures, submitted to Astronomy and Astrophysics

References in corpus (12)

• GW170817: Observation of Gravitational Waves from a Binary Neutron Star Inspiral
• Multi-messenger Observations of a Binary Neutron Star Merger
• A Comprehensive Measurement of the Local Value of the Hubble Constant with 1 km/s/Mpc Uncertainty from the Hubble Space Telescope and the SH0ES Team
• The Zwicky Transient Facility: System Overview, Performance, and First Results
• Origin of the heavy elements in binary neutron-star mergers from a gravitational wave event
• The Combined Ultraviolet, Optical, and Near-Infrared Light Curves of the Kilonova Associated with the Binary Neutron Star Merger GW170817: Unified Data Set, Analytic Models, and Physical Implications
• Measurements of the Hubble Constant: Tensions in Perspective
• Machine Learning for the Zwicky Transient Facility
• The first six months of the Advanced LIGO's and Advanced Virgo's third observing run with GRANDMA
• Fink: early supernovae Ia classification using active learning
• GRANDMA Observations of ZTF/Fink Transients during Summer 2021
• Rubin Observatory LSST Transients and Variable Stars Roadmap

Cited by in corpus (8)

• Transient Classifiers for Fink: Benchmarks for LSST
• Anomaly Detection and Radio-frequency Interference Classification with Unsupervised Learning in Narrowband Radio Technosignature Searches
• Results from the Pan-STARRS Search for Kilonovae: Contamination by Massive Stellar Outbursts
• ORACLE: A Real-Time, Hierarchical, Deep-Learning Photometric Classifier for the LSST
• Early Identification of Optical Tidal Disruption Events: A science module for the Fink broker
• ELEPHANT: ExtragaLactic alErt Pipeline for Hostless AstroNomical Transients
• Real-Time Active Learning for optimised spectroscopic follow-up: Enhancing early SN Ia classification with the Fink broker
• Kilonova-Targeting Lightcurve Classification for Wide Field Survey Telescope