Persistent and occasional: Searching for the variable population of the ZTF/4MOST sky using ZTF Data Release 11^★,★★

¹ European Southern Observatory, Karl-Schwarzschild-Strasse 2, 85748 Garching bei München, Germany
e-mail: pasanchezsaez@gmail.com; paula.sanchezsaez@eso.org
² Millennium Institute of Astrophysics (MAS), Nuncio Monsenor Sotero Sanz 100, Of. 104, Providencia, Santiago, Chile
³ Departamento de Ingeniería Informática, Universidad de Santiago de Chile, Av. Ecuador 3659, Santiago, Chile
⁴ Instituto de Física y Astronomía, Facultad de Ciencias, Universidad de Valparaíso, Gran Bretaña No. 1111, Playa Ancha, Valparaíso, Chile
⁵ Millennium Nucleus on Transversal Research and Technology to Explore Supermassive Black Holes (TITANS), 4030000 Concepción, Chile
⁶ Instituto de Astrofísica, Facultad de Física, Pontificia Universidad Católica de Chile, Casilla 306, Santiago 22, Chile
⁷ Centro de Astroingeniería, Pontificia Universidad Católica de Chile, Av. Vicuña Mackenna 4860, 7820436 Macul, Santiago, Chile
⁸ Space Science Institute, 4750 Walnut Street, Suite 205, Boulder, Colorado 80301, USA
⁹ Department of Computer Science, Universidad de Concepción, Concepción, Chile
¹⁰ Data Science Unit, Universidad de Concepción, Edmundo Larenas 310, Concepción, Chile
¹¹ Department of Astronomy, Yale University, PO Box 208101, New Haven, CT 06520-8101, USA
¹² Department of Electrical Engineering, Universidad de Chile, Av. Tupper 2007, Santiago 8320000, Chile
¹³ Data and Artificial Intelligence Initiative (ID&IA), University of Chile, Santiago, Chile
¹⁴ Center for Mathematical Modeling (CMM), Universidad de Chile, Beauchef 851, Santiago 8320000, Chile
¹⁵ Instituto de Informática, Facultad de Ciencias de la Ingeniería, Universidad Austral de Chile, General Lagos 2086, Valdivia, Chile
¹⁶ Departamento de Astronomía, Universidad de Chile, Casilla 36D, Santiago, Chile
¹⁷ Departamento de Ciencias Fisícas, Universidad Andres Bello, Avda. Republica 252, Santiago, Chile

Received: 3 February 2023
Accepted: 9 April 2023

Abstract

Aims. We present a variability-, color-, and morphology-based classifier designed to identify multiple classes of transients and persistently variable and non-variable sources from the Zwicky Transient Facility (ZTF) Data Release 11 (DR11) light curves of extended and point sources. The main motivation to develop this model was to identify active galactic nuclei (AGN) at different redshift ranges to be observed by the 4MOST Chilean AGN/Galaxy Evolution Survey (ChANGES). That being said, it also serves as a more general time-domain astronomy study.

Methods. The model uses nine colors computed from CatWISE and Pan-STARRS1 (PS1), a morphology score from PS1, and 61 single-band variability features computed from the ZTF DR11 g and r light curves. We trained two versions of the model, one for each ZTF band, since ZTF DR11 treats the light curves observed in a particular combination of field, filter, and charge-coupled device (CCD) quadrant independently. We used a hierarchical local classifier per parent node approach-where each node is composed of a balanced random forest model. We adopted a taxonomy with 17 classes: non-variable stars, non-variable galaxies, three transients (SNIa, SN-other, and CV/Nova), five classes of stochastic variables (lowz-AGN, midz-AGN, highz-AGN, Blazar, and YSO), and seven classes of periodic variables (LPV, EA, EB/EW, DSCT, RRL, CEP, and Periodic-other).

Results. The macro-averaged precision, recall, and F1-score are 0.61, 0.75, and 0.62 for the g-band model, and 0.60, 0.74, and 0.61, for the r-band model. When grouping the four AGN classes (lowz-AGN, midz-AGN, highz-AGN, and Blazar) into one single class, its precision-recall, and F1-score are 1.00, 0.95, and 0.97, respectively, for both the g and r bands. This demonstrates the good performance of the model in classifying AGN candidates. We applied the model to all the sources in the ZTF/4MOST overlapping sky (−28 ≤ Dec ≤ 8.5), avoiding ZTF fields that cover the Galactic bulge (|gal_b| ≤ 9 and gal_l ≤ 50). This area includes 86 576 577 light curves in the g band and 140 409 824 in the r band with 20 or more observations and with an average magnitude in the corresponding band lower than 20.5. Only 0.73% of the g-band light curves and 2.62% of the r-band light curves were classified as stochastic, periodic, or transient with high probability (P_init ≥ 0.9). Even though the metrics obtained for the two models are similar, we find that, in general, more reliable results are obtained when using the g-band model. With it, we identified 384 242 AGN candidates (including low-, mid-, and high-redshift AGN and Blazars), 287 156 of which have P_init ≥ 0.9.