New algorithm efficiently detects radio sources in MWA 32T data
With the world awaiting construction of the ultimate radio telescope, the Square Kilometre Array (SKA), the concept of interferometry has found unprecedented attention. In short, we can achieve a similar sensitivity, resolution, and collecting area as that of a much larger radio telescope by simultaneously collecting data from an array of smaller instruments, defined by the observing frequency and maximum distance between the individual instruments. Things become tricky, however, when the data streams from these individual telescopes have to be combined, compared, and corrected for directionality and polarisation to reliably detect radio sources in the Universe.
In a new publication (The Astrophysical Journal 759-17), an international team led by the University of Washington with a number of our CAASTRO researchers on board now present a neat new technique that deals with these data streams in a very efficient way. Speed and precision are two key factors to trade off in analysing the immense amounts of data generated by next-generation radio telescopes, such as the Murchison Widefield Array (MWA) in Western Australia. For their paper, Sullivan et al. only needed five minutes of observing time with the 32-antenna MWA prototype to collect enough data to test their new algorithm to the fullest extent.
Despite being called ‘visibilities’, not much is visible in the noisy raw data that come straight from the MWA. A whole lot of complex maths is required to interpret foreground radio interference, produce images and identify false objects, for which a number of smart algorithms have been developed over the years. The new algorithm described by Sullivan et al. – called Fast Holographic Deconvolution – creates holographic maps that contain directionality and polarisation information for each individual antenna. Sullivan et al. found that the algorithm performs very well, reducing the processing time of huge data sets to as little as 1% compared to previous tools. And in addition to being fast, the algorithm was also successful in detecting 2342 candidate objects for radio sources in the data.
Ian Sullivan, Miguel Morales, Bryna Hazelton, Wayne Arcus, David Barnes, Gianni Bernardi, Frank Briggs, Judd D. Bowman, John Bunton, Roger Cappallo, Brian Corey, Avinash Deshpande, Ludi deSouza, David Emrich, B. M. Gaensler, Robert Goeke, Lincoln Greenhill, David Herne, Jacqueline Hewitt, Melanie Johnston-Hollitt, David Kaplan, Justin Kasper, Barton Kincaid, Ronald Koenig, Eric Kratzenberg, Colin Lonsdale, Mervyn Lynch, Russell McWhirter, Daniel Mitchell, Edward Morgan, Divya Oberoi, Stephen Ord, Joseph Pathikulangara, Thiagaraj Prabu, Ron Remillard, Alan Rogers, Anish Roshi, Joseph Salah, Robert Sault, Udaya Shankar, K. Srivani, Jamie Stevens, Ravi Subrahmanyan, Steven Tingay, Randall Wayth, Mark Waterson, Rachel Webster, Alan Whitney, Andrew Williams, Chris Williams, Stuart Wyithe in ApJ 759: