Finding active galactic nuclei through Fink by Etienne Russeil et al. on Tuesday 22 November

We present the Active Galactic Nuclei (AGN) classifier as currently

implemented within the Fink broker. Features were built upon summary statistics

of available photometric points, as well as color estimation enabled by

symbolic regression. The learning stage includes an active learning loop, used

to build an optimized training sample from labels reported in astronomical

catalogs. Using this method to classify real alerts from the Zwicky Transient

Facility (ZTF), we achieved 98.0% accuracy, 93.8% precision and 88.5% recall.

We also describe the modifications necessary to enable processing data from the

upcoming Vera C. Rubin Observatory Large Survey of Space and Time (LSST), and

apply them to the training sample of the Extended LSST Astronomical Time-series

Classification Challenge (ELAsTiCC). Results show that our designed feature

space enables high performances of traditional machine learning algorithms in

this binary classification task.

arXiv: http://arxiv.org/abs/http://arxiv.org/abs/2211.10987v1