The QUEST-La Silla AGN Variability Survey: Selection of AGN Candidates through Optical Variability

Abstract

We used data from the QUEST-La Silla Active Galactic Nuclei (AGN) variability survey to construct light curves for 208,583 sources over ∼ 70 deg 2 , with a a limiting magnitude r ∼ 21. Each light curve has at least 40 epochs and a length of ≥ 200 days. We implemented a Random Forest algorithm to classify our objects as either AGN or non-AGN according to their variability features and optical colors, excluding morphology cuts. We tested three classifiers, one that only includes variability features (RF1), one that includes variability features and also r −i and i−z colors (RF2), and one that includes variability features and also g−r, r−i, and i−z colors (RF3). We obtained a sample of high probability candidates (hp-AGN) for each classifier, with 5,941 candidates for RF1, 5,252 candidates for RF2, and 4,482 candidates for RF3. We divided each sample according to their g − r colors, defining blue (g − r ≤ 0.6) and red sub-samples (g − r > 0.6). We find that most of the candidates known from the literature belong to the blue sub-samples, which is not necessarily surprising given that, unlike for many literature studies, we do not cut our sample to point-like objects. This means that we can select AGN that have a significant contribution from redshifted starlight in their host galaxies. In order to test the efficiency of our technique we performed spectroscopic follow-up, confirming the AGN nature of 44 among 54 observed sources (81.5% of efficiency). From the campaign we concluded that RF2 provides the purest sample of AGN candidates.

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

“…The four diagrams show that our RF5 AGN candidates are generally characterized by redder colors than the AGN in our LS. This suggests that variability-based selection is quite effective in identifying host-dominated AGN, consistent with the findings of Sánchez-Sáez et al (2019, and highlights the strength of variability-based selection of AGN over color selection, which easily misses host-dominated AGN as their colors are similar to those of inactive galaxies.…”

“…The code we use to apply an RF algorithm to our sample of sources takes its cue from the one used in Sánchez-Sáez et al (2019). It is based on the use of the Python RF classifier library 4 included in the scikit-learn library, which provides a number of tools for machine learning-based data analysis (Pedregosa et al 2011).…”

“…In this work we test the performance of a random forest (RF; Breiman 2001) classifier to identify optically variable AGN in the same dataset used in D19. Sánchez-Sáez et al (2019) demonstrated that an RF algorithm making use of variability plus optical color features is able to identify variable AGN with 81.5% efficiency. The present project applies a similar method to the VST-COSMOS dataset.…”

SN 2019hcc: a Type II supernova displaying early O ii lines

“…The spectra were reduced using the PESSTO NTT pipeline. 1 There was also one spectrum taken by the Goodman High Throughput Spectrograph at the Southern Astrophysical Research telescope (SOAR) (Clemens, Crain & Anderson 2004), reduced using the dedicated pipeline (Sánchez-Sáez et al 2019). The final reduced and calibrated spectra will be available on the Weizmann Interactive Supernova Data Repository (WISeREP; .…”

A status report on AGN variability

“…More recently Sánchez-Sáez et al (2019) have shown that using variability and colors not only is more effective than using colors only, but that a new population of redder AGN can be identified. This population appears to be dominated by low-luminosity AGN, with colors some times completely dominated by the host emission.…”