The 154 MHz radio sky observed by the Murchison Widefield Array: noise, confusion, and first source count analyses

Abstract

We analyse a 154 MHz image made from a 12 h observation with the Murchison Widefield Array (MWA) to determine the noise contribution and behaviour of the source counts down to 30 mJy. The MWA image has a bandwidth of 30.72 MHz, a field-of-view within the half-power contour of the primary beam of 570 deg 2 , a resolution of 2.3 arcmin and contains 13,458 sources above 5σ. The rms noise in the centre of the image is 4 − 5 mJy/beam. The MWA counts are in excellent agreement with counts from other instruments and are the most precise ever derived in the flux density range 30-200 mJy due to the sky area covered. Using the deepest available source count data, we find that the MWA image is affected by sidelobe confusion noise at the ≈ 3.5 mJy/beam level, due to incompletely-peeled and out-of-image sources, and classical confusion becomes apparent at ≈ 1.7 mJy/beam. This work highlights that (i) further improvements in ionospheric calibration and deconvolution imaging techniques would be required to probe to the classical confusion limit and (ii) the shape of low-frequency source counts, including any flattening towards lower flux densities, must be determined from deeper ≈ 150 MHz surveys as it cannot be directly inferred from higher frequency data.

The GMRT 150 MHz all-sky radio survey

“…150 MHz Euclidean normalized differential source counts as derived from the TGSS ADR1 source catalog (black stars with 1-sigma poissonian error bars), covering the highly complete flux range from 100 mJy to 100 Jy with 20 logarithmic flux bins. Overplotted are various other source counts for this frequency from literature, namely source counts from both a single deep GMRT integration and a larger-area GMRT survey centered on the Boötes field (Intema et al 2011;Williams et al 2013, red and magenta dots, respectively), source counts from the 7C survey (McGilchrist et al 1990;Hales et al 2007, blue and green dots, respectively), recent source counts from deep, small-area surveys with the LOFAR HBA system Mahony et al 2016;Hardcastle et al 2016, black, yellow, and gray dots, respectively), and source counts from the MWA GLEAM survey as well as deep, single-pointing MWA survey (Hurley-Walker et al 2017;Franzen et al 2016, open triangles and dots, respectively). Some literature points with very large uncertainties have been omitted.…”

Fundamental uncertainty levels of 21 cm power spectra from a delay analysis

“…Choosing the optimal interferometer design to maximize power spectrum sensitivity requires balancing the effects of antenna positions, receiver element, and computational requirements of the design. The most significant factors that need to be reduced are thermal noise and foreground power (Franzen et al 2016;Yatawatta et al 2013;Bernardi et al 2010;Jelić et al 2008), so much work has focused on these effects. Short baselines are more sensitive to diffuse structures like the EoR signal, and a random antenna distribution typically improves imaging capability, which helps foreground modeling and subtraction (Lidz et al 2008).…”

The impact of interference excision on 21-cm epoch of reionization power spectrum analyses

“…Although an extensive model of the NCP field is available, we chose to use a simulated point-source model based on a population study, which prevents selection effects and artefacts in the model. We use a simple randomly generated population distribution that follows the empirically determined distribution by Franzen et al (2016):…”