Search for: All records

Quasars emission is highly variable, and this variability gives us clues to understand the accretion process onto supermassive black holes. We can expect variability properties to correlate with the main physical properties of the accreting black hole, i.e. its mass and accretion rate. It has been established that the relative amplitude of variability anticorrelates with the accretion rate. The dependence of the variance on black hole mass has remained elusive, and contradicting results, including positive, negative, or no correlation, have been reported. In this work, we show that the key to these contradictions lies in the times-cales of variability studied (e.g. the length of the light curves available). By isolating the variance on different time-scales in well-defined mass and accretion rate bins we show that there is indeed a negative correlation between black hole mass and variance and that this anticorrelation is stronger for shorter time-scale fluctuations. The behaviour can be explained in terms of a universal variability power spectrum for all quasars, resembling a broken power law where the variance is constant at low temporal frequencies and then drops continuously for frequencies higher than a characteristic (break) frequency fb, where fb correlates with the black hole mass. Furthermore, to explain all the variance results presented here, not only the normalization of this power spectrum must anticorrelate with the accretion rate, but also the shape of the power spectra at short time-scales must depend on this parameter as well.

 

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. 

The scarce optical variability studies in spectrally classified Type 2 active galactic nuclei (AGNs) have led to the discovery of anomalous objects that are incompatible with the simplest unified models (UMs). This paper focuses on the exploration of different variability features that allow to distinguish between obscured, Type 2 AGNs and the variable, unobscured Type 1s. We analyse systematically the Zwicky Transient Facility, 2.5-yr-long light curves of ∼15 000 AGNs from the Sloan Digital Sky Survey Data Release 16, which are generally considered Type 2s due to the absence of strong broad emission lines (BELs). Consistent with the expectations from the UM, the variability features are distributed differently for distinct populations, with spectrally classified weak Type 1s showing one order of magnitude larger variances than the Type 2s. We find that the parameters given by the damped random walk model lead to broader H α equivalent width for objects with τg > 16 d and long-term structure function SF∞, g > 0.07 mag. By limiting the variability features, we find that ∼11 per cent of Type 2 sources show evidence for optical variations. A detailed spectral analysis of the most variable sources (∼1 per cent of the Type 2 sample) leads to the discovery of misclassified Type 1s with weak BELs and changing-state candidates. This work presents one of the largest systematic investigations of Type 2 AGN optical variability to date, in preparation for future large photometric surveys.

 

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.

 

We present the second and final release of optical spectroscopy of Type Ia supernovae (SNe Ia) obtained during the first and second phases of the Carnegie Supernova Project (CSP-I and CSP-II). The newly released data consist of 148 spectra of 30 SNe Ia observed in the course of CSP-I and 234 spectra of 127 SNe Ia obtained during CSP-II. We also present 216 optical spectra of 46 historical SNe Ia, including 53 spectra of 30 SNe Ia observed by the Calán/Tololo Supernova Survey. We combine these observations with previously published CSP data and publicly available spectra to compile a large sample of measurements of spectroscopic parameters at maximum light, consisting of pseudo-equivalent widths and expansion velocities of selected features for 232 CSP and historical SNe Ia (including more than 1000 spectra). Finally, we review some of the strongest correlations between spectroscopic and photometric properties of SNe Ia. Specifically, we define two samples: one consisting of SNe Ia discovered by targeted searches (most of them CSP-I objects) and the other composed of SNe Ia discovered by untargeted searches, which includes most of the CSP-II objects. The analyzed correlations are similar for both samples. We find a larger incidence of SNe Ia belonging to the cool and broad-line Branch subtypes among the events discovered by targeted searches, shallow-silicon SNe Ia are present with similar frequencies in both samples, while core normal SNe Ia are more frequent in untargeted searches.

 

We present deep optical and near-infrared photometry of UID 30901, a superluminous supernova (SLSN) discovered during the UltraVISTA survey. The observations were obtained with VIRCAM (YJHKs) mounted on the VISTA telescope, DECam (griz) on the Blanco telescope, and SUBARU Hyper Suprime-Cam (HSC; grizy). These multiband observations comprise +700 d making UID 30901 one of the best photometrically followed SLSNe to date. The host galaxy of UID 30901 is detected in a deep HST F814W image with an AB magnitude of 27.3 ± 0.2. While no spectra exist for the SN or its host galaxy, we perform our analysis assuming z = 0.37, based on the photometric redshift of a possible host galaxy found at a projected distance of 7 kpc. Fitting a blackbody to the observations, the radius, temperature, and bolometric light curve are computed. We find a maximum bolometric luminosity of 5.4 ± 0.34 × 1043 erg s−1. A flattening in the light curve beyond 600 d is observed and several possible causes are discussed. We find the observations to clearly favour an SLSN type I, and plausible power sources such as the radioactive decay of 56Ni and the spin-down of a magnetar are compared to the data. We find that the magnetar model yields a good fit to the observations with the following parameters: a magnetic field B = 1.4 ± 0.3 × 1014 G, spin period of P = 6.0 ± 0.1 ms, and ejecta mass $M_{\mathrm{ ej}} = 11.9^{+4.8}_{-6.4} \,\mathrm{ M}_{\odot }$.