Improving the selection of changing-look AGNs through multiwavelength photometric variability

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 multiwavelength 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 multiwavelength 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.

Automated summary

In this study, second epoch optical spectra for 30 changing-look (CL) candidates were presented. These candidates were found by searching for Type-1 optical variability in a sample of active galactic nuclei (AGNs) spectroscopically classified as Type 2. Through the use of a random-forest-based light-curve classifier and spectroscopic follow-up, 50% of the candidates were confirmed as turning-on CLs. The study also included a multiwavelength variability analysis to improve the selection method and understand the nature of not-confirmed CL candidates.