Speaker
Description
We present results of an optical variability study of 44 newly identified blazar candidates behind the Magellanic Clouds. The sample contains candidates for 27 flat spectrum radio quasars (FSRQs) and 17 BL Lacertae objects (BL Lacs), with nine of them recognized as blazars, while the classification of the remaining objects is still uncertain. All objects possess high photometric accuracy and frequently sampled optical light curves (LCs) from the long-term (∼2 decades) monitoring conducted by the Optical Gravitational Lensing Experiment in V and I filters. The LCs were modelled with the Continuous-time Auto-Regressive Moving Average (CARMA) process, using the publicly available Markov Chain Monte Carlo sampler described by Kelly et al. (2014) and with the Lomb-Scargle (LS) periodogram. The CARMA models allow us to investigate variability features of irregularly sampled LCs, especially their power spectral density (PSD), to determine variability-based classification of astrophysical objects and to detect quasi-periodic oscillations (QPOs). We found that some of the examined objects require high-order fits, implying a deviation from the simple single-Lorentzian PSD. The power law PSD is indicative of a self-affine stochastic process characterised by the Hurst exponent H, underlying the observed variability. An estimation of the H values was performed with a wavelet lifting transform. We find that most objects have H ≤ 0.5, indicating short-term memory, but four BL Lacs and two FSRQs have H > 0.5, implying long-term memory. The higher-order CARMA fits suggest there are additional variations present in blazar jets and/or accretion discs that affect both the overall shape of the PSDand can give rise to QPOs. The non-power law features are also visible in some of the LS periodograms, and signs of flattening of the PSD at low frequencies observed in some of the CARMA fits hint at the blazar nature of the objects.
