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Register a new userThe ATLAS All-Sky Stellar Reference Catalog
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The Asteroid Terrestrial-impact Last Alert System (ATLAS) observes most of the sky every night in search of dangerous asteroids. Its data are also used to search for photometric variability, where sensitivity to variability is limited by photometric accuracy. Since each exposure spans 7.6 deg corner to corner, variations in atmospheric transparency in excess of 0.01 mag are common, and 0.01 mag photometry cannot be achieved by using a constant flat field calibration image. We therefore have assembled an all-sky reference catalog of approximately one billion stars to m~19 from a variety of sources to calibrate each exposures astrometry and photometry. Gaia DR2 is the source of astrometry for this ATLAS Refcat2. The sources of g, r, i, z photometry include Pan-STARRS DR1, the ATLAS Pathfinder photometry project, ATLAS re-flattened APASS data, SkyMapper DR1, APASS DR9, the Tycho-2 catalog, and the Yale Bright Star Catalog. We have attempted to make this catalog at least 99% complete to m<19, including the brightest stars in the sky. We believe that the systematic errors are no larger than 5 millimag RMS, although errors are as large as 20 millimag in small patches near the galactic plane.
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Technology has advanced to the point that it is possible to image the entire sky every night and process the data in real time. The sky is hardly static: many interesting phenomena occur, including variable stationary objects such as stars or QSOs, transient stationary objects such as supernovae or M dwarf flares, and moving objects such as asteroids and the stars themselves. Funded by NASA, we have designed and built a sky survey system for the purpose of finding dangerous near-Earth asteroids (NEAs). This system, the Asteroid Terrestrial-impact Last Alert System (ATLAS), has been optimized to produce the best survey capability per unit cost, and therefore is an efficient and competitive system for finding potentially hazardous asteroids (PHAs) but also for tracking variables and finding transients. While carrying out its NASA mission, ATLAS now discovers more bright ($m < 19$) supernovae candidates than any ground based survey, frequently detecting very young explosions due to its 2 day cadence. ATLAS discovered the afterglow of a gamma-ray burst independent of the high energy trigger and has released a variable star catalogue of 5$times10^{6}$ sources. This, the first of a series of articles describing ATLAS, is devoted to the design and performance of the ATLAS system. Subsequent articles will describe in more detail the software, the survey strategy, ATLAS-derived NEA population statistics, transient detections, and the first data release of variable stars and transient lightcurves.
Most of the sky has been imaged with NOAOs telescopes from both hemispheres. While the large majority of these data were obtained for PI-led projects and almost all of the images are publicly available, only a small fraction have been released to the community via well-calibrated and easily accessible catalogs. We are remedying this by creating a catalog of sources from most of the public data taken on the CTIO-4m+DECam and the KPNO-4m+Mosaic3. This catalog, called the NOAO Source Catalog (NSC), contains over 2.9 billion unique objects, 34 billion individual source measurements, covers ~30,000 square degrees of the sky, has depths of ~23rd magnitude in most broadband filters with ~1-2% photometric precision, and astrometric accuracy of ~7 mas. In addition, ~2 billion objects and ~21,000 square degrees of sky have photometry in three or more bands. The NSC will be useful for exploring stellar streams, dwarf satellite galaxies, QSOs, high-proper motion stars, variable stars and other transients. The NSC catalog is publicly available via the NOAO Data Lab service.
We announce the second data release (DR2) of the NOIRLab Source Catalog (NSC), using 412,116 public images from CTIO-4m+DECam, the KPNO-4m+Mosaic3 and the Bok-2.3m+90Prime. NSC DR2 contains over 3.9 billion unique objects, 68 billion individual source measurements, covers $approx$35,000 square degrees of the sky, has depths of $approx$23rd magnitude in most broadband filters with $approx$1-2% photometric precision, and astrometric accuracy of $approx$7 mas. Approximately 1.9 billion objects within $approx$30,000 square degrees of sky have photometry in three or more bands. There are several improvements over NSC DR1. DR2 includes 156,662 (61%) more exposures extending over 2 more years than in DR1. The southern photometric zeropoints in $griz$ are more accurate by using the Skymapper DR1 and ATLAS-Ref2 catalogs, and improved extinction corrections were used for high-extinction regions. In addition, the astrometric accuracy is improved by taking advantage of Gaia DR2 proper motions when calibrating the WCS of individual images. This improves the NSC proper motions to $sim$2.5 mas/yr (precision) and $sim$0.2 mas/yr (accuracy). The combination of sources into unique objects is performed using a DBSCAN algorithm and mean parameters per object (such as mean magnitudes, proper motion, etc.) are calculated more robustly with outlier rejection. Finally, eight multi-band photometric variability indices are calculated for each object and variable objects are flagged (23 million objects). NSC DR2 will be useful for exploring solar system objects, stellar streams, dwarf satellite galaxies, QSOs, variable stars, high-proper motion stars, and transients. Several examples of these science use cases are presented. The NSC DR2 catalog is publicly available via the NOIRLabs Astro Data Lab science platform.
With growing data volumes from synoptic surveys, astronomers must become more abstracted from the discovery and introspection processes. Given the scarcity of follow-up resources, there is a particularly sharp onus on the frameworks that replace these human roles to provide accurate and well-calibrated probabilistic classification catalogs. Such catalogs inform the subsequent follow-up, allowing consumers to optimize the selection of specific sources for further study and permitting rigorous treatment of purities and efficiencies for population studies. Here, we describe a process to produce a probabilistic classification catalog of variability with machine learning from a multi-epoch photometric survey. In addition to producing accurate classifications, we show how to estimate calibrated class probabilities, and motivate the importance of probability calibration. We also introduce a methodology for feature-based anomaly detection, which allows discovery of objects in the survey that do not fit within the predefined class taxonomy. Finally, we apply these methods to sources observed by the All Sky Automated Survey (ASAS), and unveil the Machine-learned ASAS Classification Catalog (MACC), which is a 28-class probabilistic classification catalog of 50,124 ASAS sources. We estimate that MACC achieves a sub-20% classification error rate, and demonstrate that the class posterior probabilities are reasonably calibrated. MACC classifications compare favorably to the classifications of several previous domain-specific ASAS papers and to the ASAS Catalog of Variable Stars, which had classified only 24% of those sources into one of 12 science classes. The MACC is publicly available at http://www.bigmacc.info.
We present a description of the Prototype All-Sky Imager (PASI), a backend correlator and imager of the first station of the Long Wavelength Array (LWA1). PASI cross-correlates a live stream of 260 dual-polarization dipole antennas of the LWA1, creates all-sky images, and uploads them to the LWA-TV website in near real-time. PASI has recorded over 13,000 hours of all-sky images at frequencies between 10 and 88 MHz creating opportunities for new research and discoveries. We also report rate density and pulse energy density limits on transients at 38, 52, and 74 MHz, for pulse widths of 5 s. We limit transients at those frequencies with pulse energy densities of $>2.7times 10^{-23}$, $>1.1times 10^{-23}$, and $>2.8times 10^{-23}$ J m$^{-2}$ Hz$^{-1}$ to have rate densities $<1.2times10^{-4}$, $<5.6times10^{-4}$, and $<7.2times10^{-4}$ yr$^{-1}$ deg$^{-2}$
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