We present reverberation mapping results from the first year of combined spectroscopic and photometric observations of the Sloan Digital Sky Survey Reverberation Mapping Project. We successfully recover reverberation time delays between the g+i band emission and the broad Hβ emission line for a total of 44 quasars, and for the broad Hα emission line in 18 quasars. Time delays are computed using the JAVELIN and CREAM software and the traditional interpolated cross-correlation function (ICCF): using well-defined criteria, we report measurements of 32 Hβ and 13 Hα lags with JAVELIN, 42 Hβ and 17 Hα lags with CREAM, and 16 Hβ and eight Hα lags with the ICCF. Lag values are generally consistent among the three methods, though we typically measure smaller uncertainties with JAVELIN and CREAM than with the ICCF, given the more physically motivated light curve interpolation and more robust statistical modeling of the former two methods. The median redshift of our Hβ-detected sample of quasars is 0.53, significantly higher than that of the previous reverberation mapping sample. We find that in most objects, the time delay of the Hα emission is consistent with or slightly longer than that of Hβ. We measure black hole masses using our measured time delays and line widths for these quasars. These black hole mass measurements are mostly consistent with expectations based on the local M BH -* s relationship, and are also consistent with single-epoch black hole mass measurements. This work increases the current sample size of reverberation-mapped active galaxies by about two-thirds and represents the first large sample of reverberation mapping observations beyond the local universe (z<0.3).

We present host stellar velocity dispersion measurements for a sample of 88 broad-line quasars at 0.1 < z < 1 (46 at z > 0.6) from the Sloan Digital Sky Survey Reverberation Mapping (SDSS-RM) project. High signalto-noise ratio coadded spectra (average S/N ≈ 30 per 69 km s −1 pixel) from SDSS-RM allowed decomposition of the host and quasar spectra, and measurements of the host stellar velocity dispersions and black hole (BH) masses using the single-epoch (SE) virial method. The large sample size and dynamic range in luminosity (L 5100 = 10 43.2−44.7 erg s −1 ) lead to the first clear detection of a correlation between SE virial BH mass and host stellar velocity dispersion far beyond the local universe. However, the observed correlation is significantly flatter than the local relation, suggesting that there are selection biases in high-z luminosity-threshold quasar samples for such studies. Our uniform sample and analysis enable an investigation of the redshift evolution of the M • − σ * relation relatively free of caveats by comparing different samples/analyses at disjoint redshifts. We do not observe evolution of the M • − σ * relation in our sample up to z ∼ 1, but there is an indication that the relation flattens towards higher redshifts. Coupled with the increasing threshold luminosity with redshift in our sample, this again suggests certain selection biases are at work, and simple simulations demonstrate that a constant M • − σ * relation is favored to z ∼ 1. Our results highlight the scientific potential of deep coadded spectroscopy from quasar monitoring programs, and offer a new path to probe the co-evolution of BHs and galaxies at earlier times.

We identify 885,503 type 1 quasar candidates to i 22  using the combination of optical and mid-IR photometry. Optical photometry is taken from the Sloan Digital Sky Survey-III: Baryon Oscillation Spectroscopic Survey (SDSS-III/BOSS), while mid-IR photometry comes from a combination of data from the Wide-field Infrared Survey Explorer (WISE) "AllWISE" data release and several large-area Spitzer Space Telescope fields. Selection is based on a Bayesian kernel density algorithm with a training sample of 157,701 spectroscopically confirmed type1 quasars with both optical and mid-IR data. Of the quasar candidates, 733,713 lack spectroscopic confirmation (and 305,623 are objects that we have not previously classified as photometric quasar candidates). These candidates include 7874 objects targeted as high-probability potential quasars with z 3.5 5 < < (of which 6779 are new photometric candidates). Our algorithm is more complete to z 3.5 > than the traditional mid-IR selection "wedges" and to z 2.2 3.5 < < quasars than the SDSS-III/BOSS project. Number counts and luminosity function analysis suggestthat the resulting catalog is relatively complete to known quasars and is identifying new high-z quasars at z 3 >. This catalog paves the way for luminosity-dependent clustering investigations of large numbers of faint, high-redshift quasars and for further machine-learning quasar selection using Spitzer and WISE data combined with other large-area optical imaging surveys.

We describe the simulated data sample for the "Photometric LSST Astronomical Time Series Classification Challenge" (PLAsTiCC), a publicly available challenge to classify transient and variable events that will be observed by the Large Synoptic Survey Telescope (LSST), a new facility expected to start in the early 2020s. The challenge was hosted by Kaggle, ran from 2018 September 28 to 2018 December 17, and included 1,094 teams competing for prizes. Here we provide details of the 18 transient and variable source models, which were not revealed until after the challenge, and release the model libraries at https://doi.org/10.5281/zenodo.2612896. We describe the LSST Operations Simulator used to predict realistic observing conditions, and we describe the publicly available SNANA simulation code used to transform the models into observed fluxes and uncertainties in the LSST passbands (ugrizy). Although PLAsTiCC has finished, the publicly available models and simulation tools are being used within the astronomy community to further improve classification, and to study contamination in photometrically identified samples of type Ia supernova used to measure properties of dark energy. Our simulation framework will continue serving as a platform to improve the PLAsTiCC models, and to develop new models.