Fast Extragalactic Transients: 

Searches for optical transients are usually performed with a cadence of days to weeks, which is optimal for supernova and similar duration discoveries.  DWF searched the fast transient sky, events with millisecond-to-hours duration.  Fast transients include fast radio bursts, supernova shock breakouts, gamma-ray bursts, flare stars, novae, and kilonovae. The fast transient sky is still largely unexplored, with only a few surveys that have placed meaningful constraints on the detection of faint extragalactic transients evolving at sub-hour timescales.  DWF is able to study a regime in the time domain that has been largely unexplored as a result of technological and facility coordination challenges.   And we do this at all wavelengths and particle and gravitational wave detectors.   In the radio, we coordinate searches for fast radio bursts in real-time with Parkes, Molonglo, ASKAP, and MeerKAT.   We coordinate searchers for high-energy fast transients with the Neil Gehrels Swift Observatory and HXMT space telescopes and the Pierre Auger Observatory, among others.  In the optical, we perform continuous, fast-cadenced obsrvations using the CTIO Dark Energy Camera wide-field camera on the 4-metre Blanco telescope in Chile and the NAOJ Hyper-SuprimeCam wide-field camera on the 8-metre Subaru telescope on the summit of Mauna Kea in Hawaii.  These extremely sensitive cameras have approximately 2-3 square degree field of views, which equates to about the area of 9-15 full Moons on the sky.  For more, see Andreoni+, to be submitted.

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Fast Galactic Transients: 

Our very own galaxy, the Milky Way, is home to billions of M-Dwarf stars with very large magnetic fields which can cause incredibly energetic and optically bright flares.  These flares can last from minutes to hours, and fade rapidly.  DWF offers an unparalleled platform to study these events at multiple wavelengths with continuous high cadenced imaging of our target fields.  We are able to not only detect rise and fade of these events, but also able to detect flare emission from sources with no visible counterparts, suspected M-Dwarfs on the very edge of our galaxy!  With these data, we are able to study the morphology of the flare light curves and model how this changes as a fraction of spectral type.  For more see Webb+, in prep. 

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Real-Time Data Processing: 

The ability to quickly detect transient sources in optical images and trigger multi-wavelength follow up is key for the discovery of fast transients.  We present the Mary pipeline, a (mostly) automated tool to discover transients during high-cadenced observations with the Dark Energy Camera (DECam) at CTIO.   Our tests of the Mary pipeline on DECam images return a false positive rate of ∼ 2.2% and a missed fraction of ∼ 3.4% obtained in less than 2 minutes, which proves the pipeline to be suitable for rapid and high-quality transient searches.  The pipeline can be adapted to search for transients in data obtained with imagers other than DECam.  Read more about the 'Mary' pipeline (Andreoni+ 2017) here.

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Data Compression and Transfer: 

Each DECam image has 70 total charge-coupled devices (CCDs) saved as a ∼1.2 gigabyte FITS file.  Near real-time data processing and fast transient candidate identifications – in minutes for rapid follow-up triggers on other telescopes – requires computational power exceeding what is currently available on-site at the CTIO observatory.  In this context, the data files need to be transmitted rapidly to Australia for supercomputing post-processing, source finding, visualisation and analysis.  This step in the search process poses a major bottleneck.  Reducing the data size helps accommodate faster data transmission.  To maximise our gain in transfer time and still achieve our science goals, we opt for 'lossy' data compression — keeping in mind that raw data is archived and can be evaluated at a later time.  Nevertheless, the levels of lossy data compression still produce a > 95% detection rate of transients in real-time processing.  Read more about the lossy data compression (Vohl+ 2017) here.

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Collaborative Workspaces to Accelerate Discovery:  

cology to the Deeper, Wider, Faster proactive, simultaneous telescope observing campaign, we have shown a dramatic reduction in the time taken to inspect DECam CCD images for potential transient candidates and to produce time-critical triggers to standby telescopes.  We also show how facilitating rapid corroboration of potential candidates and the exclusion of non-candidates improves the accuracy of detection; and establish that a practical and enjoyable workspace can improve the experience of an otherwise taxing task for astronomers.  Read more about the DWF collaborative workspace and data visualisation by Meade+ 2017 here.

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Data Visualisation to Accelerate Discovery: 

As astronomy’s data-intensive era dawns, the workflows we use to make discoveries are changing dramatically. By necessity, the traditional role of the astronomer is giving way to increased automation and AI.  However, we have much to gain by maintaining an active role for the astronomer in this new generation of data-intensive workflows and, ultimately humans are needed to assess unusual events and to determine what is 'interesting'.  To meet the needs of DWF and this new era of data-intensive real time astronomy, PhD student Sarah Hegarty developed a bespoke online tool, PerSieve, a platform  to enable a real-time visual assessment and analysis of data within automated pipelines.  For more, see Hegarty+, in prep.

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Machine Learning on DWF Data:  

DWF has gathered over 12,000 DECam images, over 15 fields.  From these high cadenced, deep images, DWF is building a database of light curves of every source in each field to be combined with other wavelength data.  This database will contain millions of objects, of static, transient and variable sources.  From this rich database of light curves, we hope to implement machine and deep learning analysis to classify known and unknown sources and predict the classifications of fast events in real-time to assist with the rapid identification of light curves during our DWF runs.  If you are interested in contributing to this work please email swebb@swin.edu.au for more information!