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 lowski 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 (τ ) and an asymptotic rms variability on long time scales (SF ∞ ). We searched for correlations between these two variability parameters and physical parameters such as luminosity and black hole mass, and rest-frame wavelength. Our analysis shows SF ∞ to increase with decreasing luminosity and rest-frame wavelength as observed previously, and without a correlation with redshift. We find a correlation between SF ∞ and black hole mass with a power law index of 0.18±0.03, independent of the anti-correlation with luminosity. We find that τ 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. However, we find an additional dependence on luminosity and/or black hole mass that cannot be explained by the trend with Eddington ratio. 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.

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, τ , and an asymptotic amplitude (structure function), SF ∞ . 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 τ 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 τ . 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 τ > 100 days. In summary, given an adequate survey cadence, photometric variability provides an even better method than color selection for separating quasars from stars.

This paper characterizes the actual science performance of the James Webb Space Telescope (JWST), as determined from the six month commissioning period. We summarize the performance of the spacecraft, telescope, science instruments, and ground system, with an emphasis on differences from pre-launch expectations. Commissioning has made clear that JWST is fully capable of achieving the discoveries for which it was built. Moreover, almost across the board, the science performance of JWST is better than expected; in most cases, JWST will go deeper faster than expected. The telescope and instrument suite have demonstrated the sensitivity, stability, image quality, and spectral range that are necessary to transform our understanding of the cosmos through observations spanning from near-earth asteroids to the most distant galaxies.

We use Sloan Digital Sky Survey (SDSS) photometry of 73 million stars to simultaneously constrain best-fit mainsequence stellar spectral energy distribution (SED) and amount of dust extinction along the line of sight toward each star. Using a subsample of 23 million stars with Two Micron All Sky Survey (2MASS) photometry, whose addition enables more robust results, we show that SDSS photometry alone is sufficient to break degeneracies between intrinsic stellar color and dust amount when the shape of extinction curve is fixed. When using both SDSS and 2MASS photometry, the ratio of the total to selective absorption, R V , can be determined with an uncertainty of about 0.1 for most stars in high-extinction regions. These fits enable detailed studies of the dust properties and its spatial distribution, and of the stellar spatial distribution at low Galactic latitudes (|b| < 30• ). Our results are in good agreement with the extinction normalization given by the Schlegel et al. (SFD) dust maps at high northern Galactic latitudes, but indicate that the SFD extinction map appears to be consistently overestimated by about 20% in the southern sky, in agreement with recent study by Schlafly et al. The constraints on the shape of the dust extinction curve across the SDSS and 2MASS bandpasses disfavor the reddening law of O'Donnell, but support the models by Fitzpatrick and Cardelli et al. For the latter, we find a ratio of the total to selective absorption to be R V = 3.0 ± 0.1(random)±0.1 (systematic) over most of the high-latitude sky. At low Galactic latitudes (|b| < 5• ), we demonstrate that the SFD map cannot be reliably used to correct for extinction because most stars are embedded in dust, rather than behind it, as is the case at high Galactic latitudes. We analyze three-dimensional maps of the best-fit R V and find that R V = 3.1 cannot be ruled out in any of the 10 SEGUE stripes at a precision level of ∼0.1-0.2. Our best estimate for the intrinsic scatter of R V in the regions probed by SEGUE stripes is ∼0.2. We introduce a method for efficient selection of candidate red giant stars in the disk, dubbed "dusty parallax relation," which utilizes a correlation between distance and the extinction along the line of sight. We make these best-fit parameters, as well as all the input SDSS and 2MASS data, publicly available in a user-friendly format. These data can be used for studies of stellar number density distribution, the distribution of dust properties, for selecting sources whose SED differs from SEDs for high-latitude main-sequence stars, and for estimating distances to dust clouds and, in turn, to molecular gas clouds.

This paper completes the series of cataclysmic variables (CVs) identified from the Sloan Digital Sky Survey (SDSS) I/II. The coordinates, magnitudes, and spectra of 33 CVs are presented. Among the 33 are eight systems known prior to SDSS (CT Ser, DO Leo, HK Leo, IR Com, V849 Her, V405 Peg, PG1230+226, and HS0943+1404), as well as nine objects recently found through various photometric surveys. Among the systems identified since the SDSS are two polar candidates, two intermediate polar candidates, and one candidate for containing a pulsating white dwarf. Our follow-up data have confirmed a polar candidate from Paper VII and determined tentative periods for three of the newly identified CVs. A complete summary table of the 285 CVs with spectra from SDSS I/II is presented as well as a link to an online table of all known CVs from both photometry and spectroscopy that will continue to be updated as future data appear.