Improving the selection of changing-look AGNs through multi-wavelength photometric variability

TL;DR

This study enhances the identification of changing-look AGNs by combining optical, MIR, and X-ray variability data with machine learning, leading to improved candidate selection and understanding of their multi-wavelength behavior.

Contribution

It introduces a multi-wavelength variability analysis combined with machine learning to better select and understand changing-look AGNs, improving upon previous optical-only methods.

Findings

Confirmed 50% of CL candidates through spectroscopy.

Confirmed CLs show stronger optical and MIR variability.

Not-confirmed CLs are consistent with weak Type 1 AGNs.

Abstract

We present second epoch optical spectra for 30 changing-look (CL) candidates found by searching for Type-1 optical variability in a sample of active galactic nuclei (AGNs) spectroscopically classified as Type 2. We use a random-forest-based light curve classifier and spectroscopic follow-up, confirming 50 per cent of candidates as turning-on CLs. In order to improve this selection method and to better understand the nature of the not-confirmed CL candidates, we perform a multi-wavelength variability analysis including optical, mid-infrared (MIR) and X-ray data, and compare the results from the confirmed and not-confirmed CLs identified in this work. We find that most of the not-confirmed CLs are consistent with weak Type 1s dominated by host-galaxy contributions, showing weaker optical and MIR variability. On the contrary, the confirmed CLs present stronger optical fluctuations and experience a long (from five to ten years) increase in their MIR fluxes and the colour W1-W2 over time. In the 0.2-2.3 keV band, at least four out of 11 CLs with available SRG/eROSITA detections have increased their flux in comparison with archival upper limits. These common features allow us to select the most promising CLs from our list of candidates, leading to nine sources with similar multi-wavelength photometric properties to our CL sample. The use of machine learning algorithms with optical and MIR light curves will be very useful to identify CLs in future large-scale surveys.