High resolution imaging of the ATLBS regions: the radio source counts

TL;DR

This study provides high-resolution radio imaging of the ATLBS regions, accurately estimating source counts down to 0.4 mJy and highlighting the importance of multi-resolution analysis and bias correction for understanding faint radio source populations.

Contribution

It presents a detailed methodology for radio source counting using multi-resolution imaging and bias correction, challenging previous estimates of source counts below 1 mJy.

Findings

ATLBS counts are lower than previous estimates below 1 mJy.

No evidence of an upturn in source counts down to 0.4 mJy.

Highlights the importance of multi-resolution and multi-wavelength data in source detection.

Abstract

The Australia Telescope Low-brightness survey (ATLBS; \cite{SESS10}) regions have been mosaic imaged at a radio frequency of 1.4 GHz with $6\arcsec$ angular resolution and 72 $\mu$Jy beam$^{-1}$ rms noise. The images (centered at RA: $00^{h}\ 35^{m}\ 00^{s}$, DEC: $-67^{\circ}\ 00^{'}\ 00^{"}$ and RA: $00^{h}\ 59^{m}\ 17^{s}$, DEC: $-67^{\circ}\ 00^{'}\ 00^{"}$ (J2000 epoch)) cover 8.42 square degrees sky area and have no artifacts or imaging errors above the image thermal noise. Multi-resolution radio and optical r-band images (made using the 4-m CTIO Blanco telescope) were used to recognize multi-component sources and prepare a source list; the detection threshold was 0.38 mJy in a low resolution radio image made with beam FWHM of $50\arcsec$. Radio source counts in the flux density range 0.4-8.7 mJy are estimated, with corrections applied for noise bias, effective area correction and resolution bias. The resolution bias is mitigated using low resolution radio images, while effects of source confusion are removed by using high resolution images for identifying blended sources. Below 1 mJy the ATLBS counts are systematically lower than the previous estimates. Showing no evidence for an upturn down to 0.4 mJy, they do not require any changes in the radio source population down to the limit of the survey. The work suggests that automated image analysis for counts may be dependent on the ability of the imaging to reproduce connecting emission with low surface brightness and on the ability of the algorithm to recognize sources, which may require that source finding algorithms effectively work with multi-resolution and multi-wavelength data. The work underscores the importance of using source lists-as opposed to component lists-and correcting for the noise bias in order to precisely estimate counts close to the image noise and determine the upturn at sub-mJy flux density.