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We present a unique method that allows the LSST to scan the sky for stellar variability on short timescales. The operational component of the strategy requires LSST to take star trail images. The image processing component uses deep learning to sift for transient events on timescales down to 10 ms. We advocate for enabling this observing mode with LSST, as coupling this capability with the LSST's tremendous 319.5 m$^2$deg$^2$ etendue will produce the first wide area optical survey of the universe on these timescales. We explain how these data will advance both planned lines of investigation and enable new research in the areas of stellar flares, cataclysmic variables, active galactic nuclei, Kuiper Belt objects, gamma-ray bursts, and fast radio bursts.

The Large Synoptic Survey Telescope (LSST) is an ambitious astronomical survey with a similarly ambitious Data Management component. Data Management for LSST includes processing on both nightly and yearly cadences to generate transient alerts, deep catalogs of the static sky, and forced photometry light-curves for billions of objects at hundreds of epochs, spanning at least a decade. The algorithms running in these pipelines are individually sophisticated and interact in subtle ways. This paper provides an overview of those pipelines, focusing more on those interactions than the details of any individual algorithm., Submitted to proceedings for ADASS XXVIII

The Large Synoptic Survey Telescope (LSST) Dark Energy Science Collaboration (DESC) will use five cosmological probes: galaxy clusters, large scale structure, supernovae, strong lensing, and weak lensing. This Science Requirements Document (SRD) quantifies the expected dark energy constraining power of these probes individually and together, with conservative assumptions about analysis methodology and follow-up observational resources based on our current understanding and the expected evolution within the field in the coming years. We then define requirements on analysis pipelines that will enable us to achieve our goal of carrying out a dark energy analysis consistent with the Dark Energy Task Force definition of a Stage IV dark energy experiment. This is achieved through a forecasting process that incorporates the flowdown to detailed requirements on multiple sources of systematic uncertainty. Future versions of this document will include evolution in our software capabilities and analysis plans along with updates to the LSST survey strategy., 32 pages + 60 pages of appendices. This is v1.0.2 of the DESC SRD, an internal collaboration document that is being made public and is not planned for submission to a journal. Data products for reproducing key plots are available on Zenodo, https://doi.org/10.5281/zenodo.1409815 ; see "Executive Summary and User Guide" for how to use and cite those products

Through matches with the Sloan Digital Sky Survey (SDSS) catalogue we identify the location of various families of astronomical objects in WISE colour space. We identify reliable indicators that separate Galactic/local from extragalactic sources and concentrate here on the objects in our Galaxy and its closest satellites. We develop colour and magnitude criteria that are based only on WISE data to select asymptotic giant branch (AGB) stars with circumstellar dust shells, and separate them into O-rich and C-rich classes. With these criteria we produce an all-sky map for the count ratio of the two populations. The map reveals differences between the Galactic disc, the Magellanic Clouds and the Sgr Dwarf Spheroidal galaxy, as well as a radial gradient in the Large Magellanic Cloud (LMC) disc. We find that the C:O number ratio for dusty AGB stars increases with distance from the LMC centre about twice as fast as measured for near-IR selected samples of early AGB stars. Detailed radiative transfer models show that WISE colours are well explained by the emission of centrally heated dusty shells where the dust has standard properties of interstellar medium (ISM) grains. The segregation of different classes of objects in WISE colour space arises from differences in properties of the dust shells: those around young stellar objects have uniform density distributions while in evolved stars they have steep radial profiles., Comment: 20 pages, 21 figures, 3 tables. Published in MNRAS. Electronic table C1 available as model_tables.dat in the source archive. This version is as close as possible to the published version, and implements comments we received from the community, and corrections made in the proofs

With upcoming all sky surveys such as LSST poised to generate a deep digital movie of the optical sky, variability-based AGN selection will enable the construction of highly-complete catalogs with minimum contamination. In this study, we generate $g$-band difference images and construct light curves for QSO/AGN candidates listed in SDSS Stripe 82 public catalogs compiled from different methods, including spectroscopy, optical colors, variability, and X-ray detection. Image differencing excels at identifying variable sources embedded in complex or blended emission regions such as Type II AGNs and other low-luminosity AGNs that may be omitted from traditional photometric or spectroscopic catalogs. To separate QSOs/AGNs from other sources using our difference image light curves, we explore several light curve statistics and parameterize optical variability by the characteristic damping timescale ($\tau$) and variability amplitude. By virtue of distinguishable variability parameters of AGNs, we are able to select them with high completeness of 93.4% and efficiency (i.e., purity) of 71.3%. Based on optical variability, we also select highly variable blazar candidates, whose infrared colors are consistent with known blazars. One third of them are also radio detected. With the X-ray selected AGN candidates, we probe the optical variability of X-ray detected optically-extended sources using their difference image light curves for the first time. A combination of optical variability and X-ray detection enables us to select various types of host-dominated AGNs. Contrary to the AGN unification model prediction, two Type II AGN candidates (out of 6) show detectable variability on long-term timescales like typical Type I AGNs. This study will provide a baseline for future optical variability studies of extended sources., Comment: 21 pages, 22 figures, Accepted for publication in The Astrophysical Journal

We use SDSS photometry of 73 million stars to simultaneously obtain best-fit main-sequence stellar energy distribution (SED) and amount of dust extinction along the line of sight towards each star. Using a subsample of 23 million stars with 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. Our results are in good agreement with the extinction normalization given by the Schlegel et al. (1998, 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 Schlafly et al. (2010). The constraints on the shape of the dust extinction curve across the SDSS and 2MASS bandpasses support the models by Fitzpatrick (1999) and Cardelli et al. (1989). For the latter, we find an $R_V=3.0\pm0.1$(random) $\pm0.1$(systematic) over most of the high-latitude sky. At low Galactic latitudes (|b, Submitted to ApJ, 55 pages, 37 figures

We describe photometric recalibration of data obtained by the asteroid survey LINEAR. Although LINEAR was designed for astrometric discovery of moving objects, the dataset described here contains over 5 billion photometric measurements for about 25 million objects, mostly stars. We use SDSS data from the overlapping ~10,000 deg^2 of sky to recalibrate LINEAR photometry, and achieve errors of 0.03 mag for sources not limited by photon statistics, with errors of 0.2 mag at r~18. With its 200 observations per object on average, LINEAR data provide time domain information for the brightest 4 magnitudes of SDSS survey. At the same time, LINEAR extends the deepest similar wide-area variability survey, the Northern Sky Variability Survey, by 3 mag. We briefly discuss the properties of about 7,000 visually confirmed periodic variables, dominated by roughly equal fractions of RR Lyrae stars and eclipsing binary stars, and analyze their distribution in optical and infra-red color-color diagrams. The LINEAR dataset is publicly available from the SkyDOT website (http://skydot.lanl.gov)., Corrected some typos to match the published version, AJ in press

(Abridged) We construct a catalog of radio sources detected by the GB6 (6 cm), FIRST and NVSS (20 cm), and WENSS (92 cm) radio surveys, and the SDSS optical survey. The 2.7 million entries in the publicly-available master catalog are comprised of the closest three FIRST to NVSS matches (within 30 arcsec) and vice-versa, and unmatched sources from each survey. Entries are supplemented by data from the other radio and optical surveys, where available. We perform data analysis a ~3000 deg^2 region of sky where the surveys overlap, which contains 140,000 NVSS-FIRST sources, of which 64,000 are detected by WENSS and 12,000 by GB6. About one third of each sample is detected by SDSS. An automated classification method based on 20 cm fluxes defines three radio morphology classes: complex, resolved, and compact. Radio color-magnitude- morphology diagrams for these classes show structure suggestive of strong underlying physical correlations. Complex and resolved sources tend to have a steep spectral slope (alpha ~ -0.8) that is nearly constant from 6 to 92 cm, while the compact class contains a significant number of flat-spectrum (alpha ~ 0) sources. In the optically-detected sample, quasars dominate the flat-spectrum compact sources while steep-spectrum and resolved objects contain substantial numbers of both quasars and galaxies. Differential radio counts of quasars and galaxies are similar at bright flux levels (>100 mJy at 20 cm), while at fainter levels the quasar counts are significantly reduced below galaxy counts. The optically-undetected sample is strongly biased toward steep-spectrum sources. In samples of quasars and galaxies with SDSS spectra, we find that radio properties such as spectral slope, morphology, and radio loudness are correlated with optical color and luminosity., 42 pages, 22 figures, 8 tables; a version with high resolution figures is available at http://www.astro.washington.edu/users/akimball/radiocat/ . Published in the Astronomical Journal. Replacement comment: data URL has changed