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We present a systematic evaluation of JPEG2000 (ISO/IEC 15444) as a transport data format to enable rapid remote searches for fast transient events as part of the Deeper Wider Faster programme. Deeper Wider Faster programme uses ~20 telescopes from radio to gamma rays to perform simultaneous and rapid-response follow-up searches for fast transient events on millisecond-to-hours timescales. Deeper Wider Faster programme search demands have a set of constraints that is becoming common amongst large collaborations. Here, we focus on the rapid optical data component of Deeper Wider Faster programme led by the Dark Energy Camera at Cerro Tololo Inter-American Observatory. Each Dark Energy Camera image has 70 total coupled-charged devices 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 Cerro Tololo Inter-American Observatory. In this context, data files need to be transmitted rapidly to a foreign location for supercomputing post-processing, source finding, visualisation and analysis. This step in the search process poses a major bottleneck, and 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. We evaluate how lossy JPEG2000 compression affects the process of finding transients, and find only a negligible effect for compression ratios up to ~25:1. We also find a linear relation between compression ratio and the mean estimated data transmission speed-up factor. Adding highly customised compression and decompression steps to the science pipeline considerably reduces the transmission time—validating its introduction to the Deeper Wider Faster programme science pipeline and enabling science that was otherwise too difficult with current technology.
By applying a display ecology 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. We provide a critical road test of two advanced displays in a research context—a rare opportunity to demonstrate how they can be used rather than simply discuss how they might be used to accelerate discovery.
Radio survey datasets comprise an increasing number of individual observations stored as sets of multidimensional data. In large survey projects, astronomers commonly face limitations regarding: 1) interactive visual analytics of sufficiently large subsets of data; 2) synchronous and asynchronous collaboration; and 3) documentation of the discovery workflow. To support collaborative data inquiry, we present encube, a large-scale comparative visual analytics framework. encube can utilise advanced visualization environments such as the CAVE2 (a hybrid 2D and 3D virtual reality environment powered with a 100 Tflop/s GPU-based supercomputer and 84 million pixels) for collaborative analysis of large subsets of data from radio surveys. It can also run on standard desktops, providing a capable visual analytics experience across the display ecology. encube is composed of four primary units enabling compute-intensive processing, advanced visualisation, dynamic interaction, parallel data query, along with data management. Its modularity will make it simple to incorporate astronomical analysis packages and Virtual Observatory capabilities developed within our community. We discuss how encube builds a bridge between high-end display systems (such as CAVE2) and the classical desktop, preserving all traces of the work completed on either platform – allowing the research process to continue wherever you are.
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