Damped Random Walk Research Articles

AN ALTERNATIVE APPROACH TO MEASURING REVERBERATION LAGS IN ACTIVE GALACTIC NUCLEI

Motivated by recent progress in the statistical modeling of quasar variability, we develop a new approach to measuring emission-line reverberation lags to estimate the size of broad-line regions (BLRs) in active galactic nuclei. Assuming that all emission-line light curves are scaled, smoothed, and displaced versions of the continuum, this alternative approach fits the light curves directly using a damped random walk model and aligns them to recover the time lag and its statistical confidence limits. We introduce the mathematical formalism of this approach and demonstrate its ability to cope with some of the problems for traditional methods, such as irregular sampling, correlated errors, and seasonal gaps. We redetermine the lags for 87 emission lines in 31 quasars and reassess the BLR size--luminosity relationship using 60 H-beta lags. We confirm the general results from the traditional cross-correlation methods, with a few exceptions. Our method, however, also supports a broad range of extensions. In particular, it can simultaneously fit multiple lines and continuum light curves which improves the lag estimate for the lines and provides estimates of the error correlations between them. Determining these correlations is of particular importance for interpreting emission-line velocity--delay maps. We can also include parameters for luminosity-dependent lags or line responses. We use this to detect the scaling of the BLR size with continuum luminosity in NGC 5548.

OPTIMAL TIME-SERIES SELECTION OF QUASARS

We present a novel method for the optimal selection of quasars using time-series observations in a single photometric bandpass. Utilizing the damped random walk model of Kelly et al. (2009), we parameterize the ensemble quasar structure function in Sloan Stripe 82 as a function of observed brightness. The ensemble model fit can then be evaluated rigorously for and calibrated with individual light curves with no parameter fitting. This yields a classification in two statistics --- one describing the fit confidence and one describing the probability of a false alarm --- which can be tuned, a priori, to achieve high quasar detection fractions (99% completeness with default cuts), given an acceptable rate of false alarms. We establish the typical rate of false alarms due to known variable stars as <3% (high purity). Applying the classification, we increase the sample of potential quasars relative to those known in Stripe 82 by as much as 29%, and by nearly a factor of two in the redshift range 2.5<z<3, where selection by color is extremeley inefficient. This represents 1875 new quasars in a 290 deg^2 field. The observed rates of both quasars and stars agree well with the model predictions, with >99% of quasars exhibiting the expected variability profile. We discus the utility of the method at high-redshift and in the regime of noisy and sparse data. Our time series selection complements well independent selection based on quasar colors and has strong potential for identifying high redshift quasars for BAO and other cosmology studies in the LSST era.

QUASAR SELECTION BASED ON PHOTOMETRIC VARIABILITY

We develop a method for separating quasars from other variable point sources using SDSS Stripe 82 light curve data for ~10,000 variable objects. To statistically describe quasar variability, we use a damped random walk model parametrized by a damping time scale, tau, and an asymptotic amplitude (structure function), SF_inf. With the aid of an SDSS spectroscopically confirmed quasar sample, we demonstrate that variability selection in typical extragalactic fields with low stellar density can deliver complete samples with reasonable purity (or efficiency, E). Compared to a selection method based solely on the slope of the structure function, the inclusion of the tau information boosts E from 60% to 75% while maintaining a highly complete sample (98%) even in the absence of color information. For a completeness of C=90%, E is boosted from 80% to 85%. Conversely, C improves from 90% to 97% while maintaining E=80% when imposing a lower limit on tau. With the aid of color selection, the purity can be further boosted to 96%, with C= 93%. Hence, selection methods based on variability will play an important role in the selection of quasars with data provided by upcoming large sky surveys, such as Pan-STARRS and the Large Synoptic Survey Telescope (LSST). For a typical (simulated) LSST cadence over 10 years and a photometric accuracy of 0.03 mag (achieved at i~22), C is expected to be 88% for a simple sample selection criterion of tau>100 days. In summary, given an adequate survey cadence, photometric variability provides an even better method than color selection for separating quasars from stars.

MODELING THE TIME VARIABILITY OF SDSS STRIPE 82 QUASARS AS A DAMPED RANDOM WALK

We model the time variability of ~9,000 spectroscopically confirmed quasars in SDSS Stripe 82 as a damped random walk. Using 2.7 million photometric measurements collected over 10 years, we confirm the results of Kelly et al. (2009) and Koz{\l}owski et al. (2010) that this model can explain quasar light curves at an impressive fidelity level (0.01-0.02 mag). The damped random walk model provides a simple, fast [O(N) for N data points], and powerful statistical description of quasar light curves by a characteristic time scale (tau) and an asymptotic rms variability on long time scales (SF_inf). We searched for correlations between these two variability parameters and physical parameters such as luminosity and black hole mass, and rest-frame wavelength. We find that tau increases with increasing wavelength with a power law index of 0.17, remains nearly constant with redshift and luminosity, and increases with increasing black hole mass with power law index of 0.21+/-0.07. The amplitude of variability is anti-correlated with the Eddington ratio, which suggests a scenario where optical fluctuations are tied to variations in the accretion rate. The radio-loudest quasars have systematically larger variability amplitudes by about 30%, when corrected for the other observed trends, while the distribution of their characteristic time scale is indistinguishable from that of the full sample. We do not detect any statistically robust differences in the characteristic time scale and variability amplitude between the full sample and the small subsample of quasars detected by ROSAT. Our results provide a simple quantitative framework for generating mock quasar light curves, such as currently used in LSST image simulations. (abridged)