Source counts and confusion at 72–231 MHz in the MWA GLEAM survey. (11th February 2019)

• Record Type: Journal Article
• Authors: Franzen, T. M. O.; Vernstrom, T.; Jackson, C. A.; Hurley-Walker, N.; Ekers, R. D.; Heald, G.; Seymour, N.; White, S. V.
• Is Part Of: Publications of the Astronomical Society of Australia. Volume 36(2019)
• Publication Date: 2019-02-11
• Subjects: galaxies: active, -- galaxies: statistics, -- radio continuum: galaxies, -- surveys, -- techniques: image processing; Astronomy -- Periodicals; Southern sky (Astronomy) -- Periodicals; Astrophysics -- Periodicals; Periodicals; 520.5
• DOI: 10.1017/pasa.2018.52
• Languages: English
• ISSNs: 1323-3580

Abstract

Abstract: The GaLactic and Extragalactic All-sky Murchison Widefield Array survey is a radio continuum survey at 72–231 MHz of the whole sky south of declination +30º, carried out with the Murchison Widefield Array. In this paper, we derive source counts from the GaLactic and Extragalactic All-sky Murchison data at 200, 154, 118, and 88 MHz, to a flux density limit of 50, 80, 120, and 290 mJy respectively, correcting for ionospheric smearing, incompleteness and source blending. These counts are more accurate than other counts in the literature at similar frequencies as a result of the large area of sky covered and this survey's sensitivity to extended emission missed by other surveys. At S 154 MHz > 0.5 Jy, there is no evidence of flattening in the average spectral index (α ≈ −0.8 where S ∝ v α ) towards the lower frequencies. We demonstrate that the Square Kilometre Array Design Study model by Wilman et al. significantly underpredicts the observed 154-MHz GaLactic and Extragalactic All-sky Murchison counts, particularly at the bright end. Using deeper Low-Frequency Array counts and the Square Kilometre Array Design Study model, we find that sidelobe confusion dominates the thermal noise and classical confusion at v ≳ 100 MHz due to both the limited CLEANing depth and the undeconvolved sources outside the field-of-view. We show that we can approach the theoretical noise limit using a more efficient and automated CLEAN algorithm.