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VAST: An ASKAP Survey for Variables and Slow Transients

Published online by Cambridge University Press:  15 February 2013

TARA MURPHY*
Affiliation:
Sydney Institute for Astronomy, School of Physics, The University of Sydney, NSW 2006, Australia School of Information Technologies, The University of Sydney, NSW 2006, Australia ARC Centre of Excellence for All-sky Astrophysics (CAASTRO), NSW 2016, Australia
SHAMI CHATTERJEE
Affiliation:
Department of Astronomy, Cornell University, Ithaca, NY 14853, USA
DAVID L. KAPLAN
Affiliation:
Physics Department, University of Wisconsin–Milwaukee, Milwaukee, WI 53211, USA
JAY BANYER
Affiliation:
Sydney Institute for Astronomy, School of Physics, The University of Sydney, NSW 2006, Australia ARC Centre of Excellence for All-sky Astrophysics (CAASTRO), NSW 2016, Australia
MARTIN E. BELL
Affiliation:
Sydney Institute for Astronomy, School of Physics, The University of Sydney, NSW 2006, Australia ARC Centre of Excellence for All-sky Astrophysics (CAASTRO), NSW 2016, Australia
HAYLEY E. BIGNALL
Affiliation:
ICRAR—Curtin University, GPO Box U1987A, Perth, WA 6845, Australia
GEOFFREY C. BOWER
Affiliation:
Astronomy Department, University of California, Berkeley, Berkeley, CA 94720-3411, USA
ROBERT A. CAMERON
Affiliation:
SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, CA 94025, USA
DAVID M. COWARD
Affiliation:
School of Physics, University of Western Australia, Crawley WA 6009, Australia
JAMES M. CORDES
Affiliation:
Department of Astronomy, Cornell University, Ithaca, NY 14853, USA
STEVE CROFT
Affiliation:
Astronomy Department, University of California, Berkeley, Berkeley, CA 94720-3411, USA
JAMES R. CURRAN
Affiliation:
School of Information Technologies, The University of Sydney, NSW 2006, Australia
S. G. DJORGOVSKI
Affiliation:
California Institute of Technology, Pasadena, CA 91125, USA
SEAN A. FARRELL
Affiliation:
Sydney Institute for Astronomy, School of Physics, The University of Sydney, NSW 2006, Australia ARC Centre of Excellence for All-sky Astrophysics (CAASTRO), NSW 2016, Australia
DALE A. FRAIL
Affiliation:
National Radio Astronomy Observatory, PO Box O, Socorro, NM 87801, USA
B. M. GAENSLER
Affiliation:
Sydney Institute for Astronomy, School of Physics, The University of Sydney, NSW 2006, Australia ARC Centre of Excellence for All-sky Astrophysics (CAASTRO), NSW 2016, Australia
DUNCAN K. GALLOWAY
Affiliation:
Monash Centre for Astrophysics, School of Physics and School of Mathematical Sciences, Monash University, VIC 3800, Australia
BRUCE GENDRE
Affiliation:
ASI Science Data Center, via Galileo Galilei, 00044 Frascati (RM), Italy
ANNE J. GREEN
Affiliation:
Sydney Institute for Astronomy, School of Physics, The University of Sydney, NSW 2006, Australia ARC Centre of Excellence for All-sky Astrophysics (CAASTRO), NSW 2016, Australia
PAUL J. HANCOCK
Affiliation:
Sydney Institute for Astronomy, School of Physics, The University of Sydney, NSW 2006, Australia ARC Centre of Excellence for All-sky Astrophysics (CAASTRO), NSW 2016, Australia
SIMON JOHNSTON
Affiliation:
CSIRO Astronomy and Space Science, Epping, NSW 1710, Australia
ATISH KAMBLE
Affiliation:
Physics Department, University of Wisconsin–Milwaukee, Milwaukee, WI 53211, USA
CASEY J. LAW
Affiliation:
Astronomy Department, University of California, Berkeley, Berkeley, CA 94720-3411, USA
T. JOSEPH W. LAZIO
Affiliation:
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
KITTY K. LO
Affiliation:
Sydney Institute for Astronomy, School of Physics, The University of Sydney, NSW 2006, Australia ARC Centre of Excellence for All-sky Astrophysics (CAASTRO), NSW 2016, Australia
JEAN-PIERRE MACQUART
Affiliation:
ARC Centre of Excellence for All-sky Astrophysics (CAASTRO), NSW 2016, Australia ICRAR—Curtin University, GPO Box U1987A, Perth, WA 6845, Australia
NANDA REA
Affiliation:
Institut de Ciencies de l’Espai (CSIC-IEEC), Campus UAB, Torre C5, 08193 Barcelona, Spain
UMAA REBBAPRAGADA
Affiliation:
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
CORMAC REYNOLDS
Affiliation:
ICRAR—Curtin University, GPO Box U1987A, Perth, WA 6845, Australia
STUART D. RYDER
Affiliation:
Australian Astronomical Observatory, Epping, NSW 1710, Australia
BRIAN SCHMIDT
Affiliation:
ARC Centre of Excellence for All-sky Astrophysics (CAASTRO), NSW 2016, Australia RSAA, Mount Stromlo Observatory, The Australian National University, ACT 2611, Australia
ROBERTO SORIA
Affiliation:
ICRAR—Curtin University, GPO Box U1987A, Perth, WA 6845, Australia
INGRID H. STAIRS
Affiliation:
Department of Physics and Astronomy, University of British Columbia, 6224 Agricultural Road, Vancouver, BC V6T 1Z1, Canada
STEVEN J. TINGAY
Affiliation:
ARC Centre of Excellence for All-sky Astrophysics (CAASTRO), NSW 2016, Australia ICRAR—Curtin University, GPO Box U1987A, Perth, WA 6845, Australia
ULF TORKELSSON
Affiliation:
Department of Physics, University of Gothenburg, SE 412 96 Gothenburg, Sweden
KIRI WAGSTAFF
Affiliation:
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
MARK WALKER
Affiliation:
Manly Astrophysics, 3/22 Cliff Street, Manly 2095, Australia
RANDALL B. WAYTH
Affiliation:
ICRAR—Curtin University, GPO Box U1987A, Perth, WA 6845, Australia
PETER K. G. WILLIAMS
Affiliation:
Astronomy Department, University of California, Berkeley, Berkeley, CA 94720-3411, USA
*
22 Corresponding author. Email: tara@physics.usyd.edu.au
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Abstract

The Australian Square Kilometre Array Pathfinder (ASKAP) will give us an unprecedented opportunity to investigate the transient sky at radio wavelengths. In this paper we present VAST, an ASKAP survey for Variables and Slow Transients. VAST will exploit the wide-field survey capabilities of ASKAP to enable the discovery and investigation of variable and transient phenomena from the local to the cosmological, including flare stars, intermittent pulsars, X-ray binaries, magnetars, extreme scattering events, interstellar scintillation, radio supernovae, and orphan afterglows of gamma-ray bursts. In addition, it will allow us to probe unexplored regions of parameter space where new classes of transient sources may be detected. In this paper we review the known radio transient and variable populations and the current results from blind radio surveys. We outline a comprehensive program based on a multi-tiered survey strategy to characterise the radio transient sky through detection and monitoring of transient and variable sources on the ASKAP imaging timescales of 5 s and greater. We also present an analysis of the expected source populations that we will be able to detect with VAST.

Information

Type
Research Article
Copyright
Copyright © Astronomical Society of Australia 2013 
Figure 0

Table 1. ASKAP Specifications

Figure 1

Table 2. Survey Parameters for the VAST Surveys (see Section 4.1 for Details)

Figure 2

Figure 1. An extreme scattering event in Q0954+658 at 2.7 GHz (lower) and 8.1 GHz (upper) adapted from Fiedler et al. (1987b). For clarity, an offset of 1 Jy has been added to the top trace. The strong frequency dependence of ESEs and the necessity of regular sampling of the light curves over a long period is evident. At ASKAP frequencies (∼1 GHz), the amplitude of the flux density decrease would likely have been even larger, and the flux density would likely have increased to an even higher value during the start of the event.

Figure 3

Figure 2. The parameter space for radio transients, adapted from Cordes et al. (2004). A quantity equivalent to absolute luminosity (observed flux density S multiplied by the square of the distance D2) is plotted against the dimensionless product of the emission frequency ν and the transient duration or pulse width W. In the Rayleigh–Jeans approximation, these quantities are directly proportional and related to the brightness temperature, as indicated by the diagonal lines, with T=1012 K marking the maximum brightness temperature of incoherent processes. Examples of known radio transient sources are indicated, including giant pulses and ‘nanogiant’ pulses from the Crab pulsar (Hankins et al. 2003), radio pulses from XTE J1810−197 (Camilo et al. 2006), and other pulsars from the Australia Telescope National Facility pulsar catalogue (Manchester et al. 2005); the microquasar GRS 1915+105 (Mirabel & Rodríguez 1994); radio flares from V4641 Sgr (Hjellming et al. 2000), the brown dwarf LP 944−20 (Berger et al. 2001), and the magnetar SGR 1806−20 (Gaensler et al. 2005; Cameron et al. 2005); the Galactic centre radio transient J1745−3009 (Hyman et al. 2005); pulses from the ultracool dwarf TVLM 513−46546 (Hallinan et al. 2007); as well as radio emission from the Sun and Jupiter. Red lines indicate the expected sensitivity of ASKAP to sources at distances of 10 pc, 1 kpc, and 1 Mpc.

Figure 4

Figure 3. Log two-epoch snapshot transient rate (deg−2) against log of the flux density (Jy) for surveys that report detections of transient and variables (thick lines). We also include a selection of surveys that report upper limits (thin lines with arrows); see Table 3 for survey acronyms. The surveys are coloured according to frequency: black <1 GHz, blue = 1–4.8 GHz, and red >4.8 GHz (see Table 3 for details). No corrections have been made for spectral index effects. Surveys labelled superscript ‘V’ denote detections of highly variable radio sources; those with superscript ‘T’ denote detections of transient-type sources. The VAST survey predictions are indicated and organised by sub-survey. A description of each survey is given in Section 4. In each case, the vertical segment denotes the RMS that can be achieved per observation. The horizontal segment indicates the upper limit that would be set if no transients or variables were detected in the entire survey. The joining line (between the horizontal and vertical) indicates the upper limits that could be placed by combining data from multiple 5-s snapshots up to the total available integration time per observation (see Table 3). See Section 3.1 for further discussion.

Figure 5

Table 3. Summary of Snapshot Rates for Transient and Variables Radio Sources Reported in the Literature

Figure 6

Figure 4. Illustration of the VAST pipeline functionality. A description of each stage is given in Table 4.

Figure 7

Table 4. VAST Image Products

Figure 8

Table 5. Functionality of the VAST Pipeline Components, As Illustrated in Figure 4

Figure 9

Figure 5. The FDR for each of the source-finding algorithms (from Hancock et al. 2012). No falsely detected sources are expected above an S/N of 5 for the area of the sky simulated. The non-zero rate of false detections is due to poor source characterisation.

Figure 10

Figure 6. The completeness of each catalogue (from Hancock et al. 2012) as compared to the input catalogue. The coloured curves represent the completeness of the named source finders. The black dotted curve represents the expected completeness of an ideal source finder. The plateau in completeness above and an S/N of 10 is due to sources that are poorly characterised.

Figure 11

Figure 7. Comparison of overall classification performance (accuracy percentage) by feature set (tme, stat, stat-cum, wlet, lsp, all-reps, and all) and classification algorithm (J48, Random Forest, and SVM), with results grouped by classification algorithm.

Figure 12

Figure 8. Online classification confusion matrix shown as a heat map (red = higher accuracy, blue = lower accuracy). The x-axis is the classified output class and the y-axis is the actual input class. We used 200 sources of each type and the number in each cell represents how these sources are classified. Overall accuracy at 30 d is 50%.