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arXiv 1202.0134astro-ph.CO
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ATLBS地区的高分辨率成像:射电源计数

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

K. Thorat, R. Subrahmanyan, L. Saripalli, R. D. Ekers

摘要:

澳大利亚望远镜低亮度巡天(ATLBS; \cite{SESS10})区域已在1.4 GHz的无线电频率下以$6\arcsec$角分辨率和72 $μ$ Jy光束$^{-1}$均方根噪声进行了镶嵌成像。图像(以RA:$00^{h}\ 35^{m}\ 00^{s}$,DEC:$-67^{\circ}\ 00^{'}\ 00^{"}$和RA:$00^{h}\ 59^{m}\ 17^{s}$,DEC:$-67^{\circ}\ 00^{'}\ 00^{"}$(J2000 epoch)为中心)覆盖8.42平方度的天空区域,没有高于图像热噪声的伪影或成像误差。多分辨率无线电和光学r-波段图像(使用4米CTIO布兰科望远镜)被用来识别多组分源,并准备一个源列表;检测阈值是0.38毫焦耳在低分辨率无线电图像与光束半高宽$50\arcsec$。在0.4-8.7毫焦耳的流量密度范围内的射电源计数估计,与适用于噪声偏差,有效面积校正和分辨率偏差的校正。使用低分辨率的无线电图像来减轻分辨率偏差,而通过使用高分辨率的图像来识别混合源来消除源混淆的影响。低于1 mJy的ATLBS计数系统地低于以前的估计。没有证据表明向上下降到0.4兆焦耳,他们不需要任何变化的无线电源人口下降到调查的限制。这项工作表明,计数的自动图像分析可能取决于成像再现低表面亮度连接发射的能力和算法识别源的能力,这可能需要源查找算法有效地处理多分辨率和多波长数据。这项工作强调了使用源列表的重要性,而不是组件列表,并校正噪声偏差,以精确地估计计数接近图像噪声,并确定在次毫焦耳通量密度的上升。

英文摘要:

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 $μ$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.

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