A random forest-based selection of optically variable AGN in the VST-COSMOS field^⋆

¹ Instituto de Astrofísica, Pontificia Universidad Católica de Chile, Av. Vicuña Mackenna 4860, 7820436 Macul, Santiago, Chile
e-mail: demetradecicco@gmail.com, ddecicco@astro.puc.cl
² Millennium Institute of Astrophysics (MAS), Nuncio Monseñor Sotero Sanz 100, Providencia, Santiago, Chile
³ Space Science Institute, 4750 Walnut Street, Suite 2015, Boulder, CO 80301, USA
⁴ Department of Physics, University of Napoli “Federico II”, Via Cinthia 9, 80126 Napoli, Italy
⁵ INFN – Sezione di Napoli, Via Cinthia 9, 80126 Napoli, Italy
⁶ INAF – Osservatorio Astronomico di Capodimonte, Via Moiariello 16, 80131 Napoli, Italy
⁷ Department of Astronomy and Astrophysics, 525 Davey Laboratory, The Pennsylvania State University, University Park, PA 16802, USA
⁸ Institute for Gravitation and the Cosmos, The Pennsylvania State University, University Park, PA 16802, USA
⁹ Department of Physics, 104 Davey Laboratory, The Pennsylvania State University, University Park, PA 16802, USA
¹⁰ Departamento de Ciencias Fisicas, Universidad Andres Bello, Avda. Republica 252, Santiago, Chile
¹¹ Department of Physics and Astronomy, University of the Western Cape, Private Bag X17, 7535 Bellville, Cape Town, South Africa
¹² INAF – Istituto di Radioastronomia, Via Gobetti 101, 40129 Bologna, Italy
¹³ INAF – Osservatorio Astronomico di Padova, Vicolo dell’Osservatorio 5, 35122 Padova, Italy

Received: 16 August 2020
Accepted: 11 November 2020

Abstract

Context. The survey of the COSMOS field by the VLT Survey Telescope is an appealing testing ground for variability studies of active galactic nuclei (AGN). With 54 r-band visits over 3.3 yr and a single-visit depth of 24.6 r-band mag, the dataset is also particularly interesting in the context of performance forecasting for the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST).

Aims. This work is the fifth in a series dedicated to the development of an automated, robust, and efficient methodology to identify optically variable AGN, aimed at deploying it on future LSST data.

Methods. We test the performance of a random forest (RF) algorithm in selecting optically variable AGN candidates, investigating how the use of different AGN labeled sets (LSs) and features sets affects this performance. We define a heterogeneous AGN LS and choose a set of variability features and optical and near-infrared colors based on what can be extracted from LSST data.

Results. We find that an AGN LS that includes only Type I sources allows for the selection of a highly pure (91%) sample of AGN candidates, obtaining a completeness with respect to spectroscopically confirmed AGN of 69% (vs. 59% in our previous work). The addition of colors to variability features mildly improves the performance of the RF classifier, while colors alone prove less effective than variability in selecting AGN as they return contaminated samples of candidates and fail to identify most host-dominated AGN. We observe that a bright (r ≲ 21 mag) AGN LS is able to retrieve candidate samples not affected by the magnitude cut, which is of great importance as faint AGN LSs for LSST-related studies will be hard to find and likely imbalanced. We estimate a sky density of 6.2 × 10⁶ AGN for the LSST main survey down to our current magnitude limit.