Year: 2024
Type: conference-paper
Abstract: The advent of wide-field astronomical surveys has made available large and complex data sets. However, the process of discovery and interpretation of each potentially new astronomical source is, many times, still handcrafted. In this context, machine learning algorithms have emerged as a powerful tool to mine large data sets and lower the burden on the domain expert. Active learning strategies are specially good in this task. In this article, we used the PineForest algorithm to search for superluminous supernova (SLSN) candidates in the Zwicky Transient Facility data releases. Starting from a data set of $\sim$14 million objects, and using eight previously confirmed SLSN light curves as priors, we found seven SLSN candidates. In the course of this search, we posted to the Transient Name Server three previously unreported transients: AT 2018moa and AT 2018mob as SLSN candidates, and AT 2018mpg as a possible supernova. We also discovered a re-brightening of the SLSN-I SN 2018fcg approximately 1300 days after its main burst, which poses an interesting problem regarding its origin. This study represents the first application of the PineForest algorithm for the targeted search of rare astronomical transients, specifically SLSNe.
Authors: Majumder, T., Pruzhinskaya, M. V., Ishida, E. E. O., Malanchev, K. L., Semenikhin, T. A., Krushinsky, V. V.
Institutions: University of Lethbridge, Centre National de la Recherche Scientifique, Université Clermont Auvergne, Laboratoire de Physique de Clermont Auvergne, Institut National de Physique Nucléaire et de Physique des Particules, Carnegie Mellon University, Lomonosov Moscow State University, Astronomical Institute of the Slovak Academy of Sciences
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
Funder: Russian Science Foundation
Award: G1305395183
OpenAlex ID: W4404168104