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The SkyMapper 1.3 m telescope at Siding Spring Observatory has now begun regular operations. Alongside the Southern Sky Survey, a comprehensive digital survey of the entire southern sky, SkyMapper will carry out a search for supernovae and other transients. The search strategy, covering a total footprint area of ~2000 deg2 with a cadence of $leq 5$ days, is optimised for discovery and follow-up of low-redshift type Ia supernovae to constrain cosmic expansion and peculiar velocities. We describe the search operations and infrastructure, including a parallelised software pipeline to discover variable objects in difference imaging; simulations of the performance of the survey over its lifetime; public access to discovered transients; and some first results from the Science Verification data.
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We present the second data release (DR2) of the SkyMapper Southern Survey, a hemispheric survey carried out with the SkyMapper Telescope at Siding Spring Observatory in Australia, using six optical filters: $u,v,g,r,i,z$. DR2 is the first release to go beyond the $sim$18mag (10${sigma}$) limit of the Shallow Survey released in DR1, and includes portions of the sky at full survey depth that reach >21mag in $g$ and $r$ filters. The DR2 photometry has a precision as measured by internal reproducibility of 1% in $u$ and $v$, and 0.7% in $griz$. More than 21 000 deg$^2$ have data in some filters (at either Shallow or Main Survey depth) and over 7 000 deg$^2$ have deep Main Survey coverage in all six filters. Finally, about 18 000 deg$^2$ have Main Survey data in $i$ and $z$ filters, albeit not yet at full depth. The release contains over 120 000 images, as well as catalogues with over 500 million unique astrophysical objects and nearly 5 billion individual detections. It also contains cross-matches with a range of external catalogues such as Gaia DR2, Pan-STARRS1 DR1, GALEX GUVcat, 2MASS, and AllWISE, as well as spectroscopic surveys such as 2MRS, GALAH, 6dFGS, and 2dFLenS.
Time domain astronomy has come of age with astronomers now able to monitor the sky at high cadence both across the electromagnetic spectrum and using neutrinos and gravitational waves. The advent of new observing facilities permits new science, but the ever increasing throughput of facilities demands efficient communication of coincident detections and better subsequent coordination among the scientific community so as to turn detections into scientific discoveries. To discuss the revolution occurring in our ability to monitor the Universe and the challenges it brings, on 2012 April 25-26 a group of scientists from observational and theoretical teams studying transients met with representatives of the major international transient observing facilities at the Kavli Royal Society International Centre, UK. This immediately followed the Royal Society Discussion meeting New windows on transients across the Universe held in London. Here we present a summary of the Kavli meeting at which the participants discussed the science goals common to the transient astronomy community and analysed how to better meet the challenges ahead as ever more powerful observational facilities come on stream.
At present time Robotic observatory making is of current importance. Having a large field of view and being able to point at anywhere, Robotic astronomical systems are indispensable when they looking for transients like grb, supernovae explosions, novae etc, as its impossible in these cases to foresee what you should point you telescope at and when. In work are described prompt GRB observations received on wide-field chambers MASTER-VWF, and also methods of the images analysis and transients classifications applied in real-time data processing in this experiment. For 7 months of operation 6 synchronous observations of gamma-ray burst had been made by MASTER VWF in Kislovodsk and Irkutsk. In all cases a high upper limits have been received (see tabl ref {tab_grbwf} and fig. ref {allgrb}).
We present the first data release (DR1) of the SkyMapper Southern Survey, a hemispheric survey carried out with the SkyMapper Telescope at Siding Spring Observatory in Australia. Here, we present the survey strategy, data processing, catalogue construction and database schema. The DR1 dataset includes over 66,000 images from the Shallow Survey component, covering an area of 17,200 deg$^2$ in all six SkyMapper passbands $uvgriz$, while the full area covered by any passband exceeds 20,000 deg$^2$. The catalogues contain over 285 million unique astrophysical objects, complete to roughly 18 mag in all bands. We compare our $griz$ point-source photometry with PanSTARRS1 DR1 and note an RMS scatter of 2%. The internal reproducibility of SkyMapper photometry is on the order of 1%. Astrometric precision is better than 0.2 arcsec based on comparison with Gaia DR1. We describe the end-user database, through which data are presented to the world community, and provide some illustrative science queries.
We show that multiple machine learning algorithms can match human performance in classifying transient imaging data from the Sloan Digital Sky Survey (SDSS) supernova survey into real objects and artefacts. This is a first step in any transient science pipeline and is currently still done by humans, but future surveys such as the Large Synoptic Survey Telescope (LSST) will necessitate fully machine-enabled solutions. Using features trained from eigenimage analysis (principal component analysis, PCA) of single-epoch g, r and i-difference images, we can reach a completeness (recall) of 96 per cent, while only incorrectly classifying at most 18 per cent of artefacts as real objects, corresponding to a precision (purity) of 84 per cent. In general, random forests performed best, followed by the k-nearest neighbour and the SkyNet artificial neural net algorithms, compared to other methods such as naive Bayes and kernel support vector machine. Our results show that PCA-based machine learning can match human success levels and can naturally be extended by including multiple epochs of data, transient colours and host galaxy information which should allow for significant further improvements, especially at low signal-to-noise.
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