THE COORDINATED RADIO AND INFRARED SURVEY FOR HIGH-MASS STAR FORMATION. II. SOURCE CATALOG

Raw data from the telescope were corrected for atmospheric opacity using phase monitor data and written to an AIPS-format uv file in spectral-line mode by the AIPS task FILLM. Each 8 hr block of observations was first inspected by eye and uv visibilities with large phase scatter, errant amplitudes, or system-temperature spikes were flagged out of the data set. Gross errors in the data, such as bad antennas, IFs, or polarizations were also flagged out at this stage. It was necessary to edit out the first 5 s of data from each pointing to allow for antenna settling time, reducing the on-source integration time from 45 s to 40 s. All manual flagging parameters were written to a master flag list, which was automatically applied upon restarting the pipeline. Care was taken here that the primary flux calibrators contained only good data.

The shapes of the VLA and JVLA pass-bands are different enough that a 6% closure error has been measured on JVLA–VLA baselines in continuum modes using 50 MHz bandwidths.²⁷ This error is expected to be larger at narrower bandwidths. Because we are operating in pseudo-spectral line mode the issue was mitigated by performing bandpass calibration (phase and amplitude) immediately after the initial flagging and before any further calibration. Solutions for the atmospherically and electronically induced changes in phase and amplitude were then calculated using the standard AIPS CALIB task, operating on one of the three secondary calibrators. The data were bootstrapped on to an absolute flux scale by comparing observations of the quasars 1331+305 (3C286) or 0137+331 (3C48) to their C-band model in AIPS. A global calibration table was produced, which could then be applied to the whole block.

After a first pass at calibration the OBIT flagging tasks AutoFlag and MednFlag were applied to each IF, polarization, and channel of the secondary calibrator observations. AutoFlag edits out bad visibilities based on an absolute maximum allowed value in Stokes I or V. Radio-frequency interference (RFI), e.g., from commercial broadcasting, is often highly polarized and all visibilities with Stokes V amplitude greater than 2 Jy were flagged as bad. MednFlag applies a rolling median filter to each IF and spectral channel. Visibilities were edited out if they had Stokes I values greater than five standard deviations from the median, calculated in a ∼50 s time-window. A second pass at calibration was then performed before applying a similar flagging procedure to the calibrated science observations on a field-by-field basis. Finally, all calibration and flagging tables were applied to the data and the individual forty-second pointings were split into uv FITS format files.

Metadata associated with each observation (e.g., the pointing center coordinates and number of flagged visibilities) were automatically saved in the MySQL database. The imaging procedure subsequently queried this database when building a final mosaicked image.

Fields were imaged using the OBIT Imager task, which performs imaging and deconvolution in a similar manner to the AIPS IMAGR routine. Imager automatically switches between the standard Cotton–Schwabb and SDI deconvolution algorithms (Schwab 1984; Steer et al. 1984), referred to simply as "CLEAN" in the following discussion. In addition, the task can be instructed to perform both phase and amplitude self-calibration. Imager is a complex task with many important input parameters that need careful tuning to result in a scientifically useful image. Key among these are the maximum residual flux, the threshold at which self-calibration begins, whether to perform both phase and amplitude self-calibration, and the weighting function used. The "average" CORNISH field was imaged using a Briggs robustness parameter of zero, which is a compromise between natural (high-sensitivity) and uniform (high-resolution) weighting. A minority of complex fields were treated as a special case and imaged with a custom weighting scheme; see Section 3.2.2 below. Self-calibration was performed on sources with peak fluxes greater than 30 mJy beam⁻¹. During the deconvolution process the restoring Gaussian beam was forced to be circular and have an FWHM of 15. The cell size was set at 03, over-sampling the synthesized beam. We justify these choices in the following sections.

Imager implements two algorithms that improve the dynamic range and quality of the final image. First, the AutoWindow²⁸ function dynamically places small (<20 pixels in radius) clean windows over regions of emission in the intermediate dirty map. The validity of each window is assessed periodically during the clean cycle and additional windows are created as necessary. The net effect is to CLEAN only real emission and avoid CLEANing noise, resulting in a smaller clean bias. The AutoCen²⁹ algorithm regrids uv data containing bright point sources, so their peaks fall on a pixel center. A point source at the center of a pixel can be represented as a single delta function, i.e., a single clean component, leading to a significant improvement in dynamic range. In contrast, a bright point-source offset from a pixel center requires multiple clean components, both positive and negative, to model its emission. This more complex model will inevitably suffer from rounding errors on finite-precision computers, leading to flux being scattered into the surrounding sky. The JVLA memos numbers 116 and 114 by Bill Cotton contain detailed descriptions of the AutoWindow and AutoCen algorithms.

3.2.1. Controlling the Deconvolution Algorithm

One of the most critical control parameters for the imaging task is the target maximum residual flux (MRF). The ideal value varies from field to field depending on the weather conditions, individual antenna system temperatures, and the structure and strength of emission in the field of view. To estimate the intrinsic sensitivity attainable we imaged each field in Stokes V and measured the rms noise. Sources with significant circularly polarized emission at 5 GHz are rare (Roberts et al. 1975; Homan & Lister 2006), so the rms noise measured from an uncleaned Stokes V image is expected to be comparable to the final CLEANed noise level. Because the two polarization beams of the VLA are not co-aligned on the sky they give rise to a strong instrumental polarization away from the pointing center. To compensate for this "beam squint"³⁰ only the central 2' diameter portion of each field was imaged and measured. Despite this we expect noise in the Stokes V images to be higher than the ideal in their Stokes I counterparts. In addition, the 15 restoring beam applied to the I images is larger, on average, than the unconstrained synthesized beam of the V images. This effectively smooths the noise in the Stokes I images compared to the V.

The target MRF was assumed to be equal to rms_V × f_MRF, where f_MRF is a constant multiplicative factor. The canonical value for f_MRF was determined in two ways. First, artificial point sources were injected into the uv data for an emission-free field and the OBIT Imager task was applied using a range of values for f_MRF. After each iteration the recovered flux and rms noise of the final image was measured. Figure 2 shows the results of this experiment. We found in practice that values of f_MRF = 0.8–2.0 recovered greater than 96% of the flux, within the errors. Second, we chose a representative sample of compact sources with simple morphologies and inspected the deconvolved images for residual sidelobe structure. Values of f_MRF = 0.8–1.0 were required to fully remove sidelobe structure from the images. We reimaged fields using a higher threshold where obvious clean artifacts were present. Figure 3 shows the values of f_MRF used across the survey. Most of the CORNISH area was imaged using an f_MRF = 0.8. Fields at lower declinations (including the BnA observations) required higher f_MRF values to avoid producing increased numbers of low-level artifacts in otherwise empty regions. Unresolved weak detections are dominated by the extragalactic population and hence have a flat distribution on the sky (Anglada et al. 1998; see also Sections 5.4 and 6.2.2). Based on an initial pass at the data reduction we used two further levels of f_MRF = 1.0 and 1.1, chosen to keep the number of 5σ–6σ point sources roughly constant away from the Galactic mid-plane. However, multiplier values between 0.8 and 1.1 lead to imaging artifacts in a minority of fields affected by poor calibration, containing very bright point sources (>1 Jy) or extended emission. Such fields were inspected and cleaned manually. A small proportion of fields (≪1%) were found to contain significant circularly polarized flux and were also cleaned by hand. It is likely that this emission is due to the instrumental beam squint, rather than real emission on the sky. The OBIT Imager task does not correct for beam squint, however, tests on selected CORNISH data did not find any believable Stokes V after a correction had been applied (B. Cotton 2011, private communication). The manually cleaned fields are mostly confined to high-mass star formation regions. They appear on Figure 3 as patches of red hexagons clustered around the mid-plane of the Galaxy. Values used in these cases ranged over 1.2 ⩽ f_MRF ⩽ 10, with a mean of 3.2.

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Applying the deconvolution algorithm close to the noise can result in an increase in the so-called "clean bias," an effect that results in a systematic reduction in object fluxes. It is believed to be caused by inadvertently CLEANing bright sidelobes, leading to a subtraction of real flux from astronomical objects (White et al. 1997; Condon et al. 1998). We measure the clean bias for CORNISH images in Section 5.6.

3.2.2. Imaging Extended Emission

Baseline lengths on the VLA B and BnA arrays range from approximately 300 kλ to 2 kλ, equivalent to spatial scales of 15 to 2', respectively. These array configurations sample the uv plane less well at shorter spacings and the deconvolution algorithm has difficulty reconstructing image structure on scales greater than ∼14''. The imaging procedure tends to produce "waves" or "ruffles" in the background of fields containing significant extended emission. Flux is also scattered over the image as the standard CLEAN algorithm attempts to model the emission as a series of delta functions. This can lead to high rms noise levels and multiple imaging artifacts, especially if self-calibration is allowed to run unchecked. A total of 193 fields (∼2%) were found to have poorly imaged extended emission. To combat this problem we imaged these fields using a custom weighting scheme. By default the CORNISH pipeline is configured to use robust weighting (Briggs 1995), which is a compromise between the low thermal noise of natural weighting and the high resolution of uniform weighting. In the case of uniform weighting the OBIT Imager weights each visibility by the sum of the weights present in each cell in the uv plane. For fields with extended emission we have instead weighted by the inverse of the number of visibilities within a radius of 10 cells, attenuated by a Gaussian function. For the B and BnA arrays this has the effect of weighting down the poorly sampled short spacings, similar to the effect of applying an inverse taper. We found that the rms noise and number of artifacts in the imaged fields are reduced at the expense of additional "missing" flux. The reduction in flux compared to a uniform, or robust, weighted image is highly dependent on the structure of the emission.

In order to quantify the effect of the two weighting schemes (robust = 0 and Gaussian) we imaged artificial Gaussian sources of increasing size inserted into the uv data for a blank field. The peak flux was fixed at 1.0 Jy beam⁻¹ while the FWHM was increased from 20 to 26'' in steps of 2''. Figure 4 (top panel) plots the recovered flux as a function of the source FWHM for both weighting schemes. When imaging using robust weighting, the fraction of recovered flux drops off above FWHMs greater than ∼8''. When using the Gaussian-smoothed weighting scheme this drop-off also occurs at FWHM ⩾8'', however, the fraction of recovered flux falls more rapidly. The bottom panel of Figure 4 shows the rms noise as a function of injected source size. In this case the flux density was fixed at 1.0 Jy to avoid being dynamic-range limited at higher fluxes. It is clear that the rms noise in the Gaussian weighted images is significantly lower and more stable as a function of FWHM. In practice dynamic ranges of several thousand are achieved on isolated point sources, falling to several hundred for slightly resolved sources (>1.8 arcsec).

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Figure 5 shows the effect of the two weighting schemes on the fidelity of the images. In the top panel is plotted the absolute fractional value of the residual flux remaining after the model image is subtracted from the pipeline imaged data (|S_resid|/S_model). Lower numbers mean that the image is similar to the model, while higher numbers mean that there are significant structural differences. It can be seen from the plot that the Gaussian weighting scheme is the most consistent at representing the source morphology, while the robust weighted images break down between FWHM = 8'' and 10''. To further quantify the effect we fit the pipeline imaged data with a 2D-Gaussian using the MIRIAD task imfit. The fitted versus injected FWHM is plotted in the bottom panel of Figure 5. It is again clear that the robust weighted images begin to differ from the model at FWHM ≈10'', while the Gaussian weighting scheme preserves structures out to ∼14''. Note that real-world emission with complex morphology will react differently to the Gaussian weighting scheme, depending on its visibility function.

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All fields were imaged out to a radius of 8 arcmin (∼10% power pattern) before being linearly mosaicked in the image plane onto 20' × 20' tiles oriented in equatorial (J2000) coordinates. In total 1408 tiles cover the survey area, each of which overlap by 1'. Pointing centers sit on a close-packed hexagonal grid adapted from the 1.4 GHz NVSS survey and scaled to 5 GHz. Condon et al. (1998) justify this layout in detail and Hoare et al. (2012) describe the implementation in CORNISH. Here we provide a summary for convenience. Adjacent CORNISH pointing centers are separated by 74, compared to the 89 FWHM of the primary beam at 4.86 GHz. The separation is optimized to maximize the uniformity of the noise pattern without appreciably degrading observing efficiency. At any point in the mosaic the sky brightness B is given by a weighted sum of the individual brightness values b_i contributed by the overlapping snapshots

$$\begin{equation} B = \left. \displaystyle \sum \limits _{i=1}^m\,W_i b_i \left /\right. \displaystyle \sum \limits _{i=1}^m W_i^2. \right. \end{equation} \tag{ 1 }$$

To maximize sensitivity the weighting factor W_i was set to be proportional to P(ρ), the primary beam pattern as a function of offset ρ from the pointing center. This correction is necessary as the noise is constant across a raw snapshot image and must be weighted by the square of the signal-to-noise ratio. The weighting method is implemented in the CORNISH pipeline in two steps. Individual fields are first multiplied by P(ρ) and summed onto a blank tile. This image is then divided by a "weight image" created from the sum of P²(ρ) functions (modeled by Gaussians for the VLA). The resultant data product is a mosaicked image which has been primary beam corrected (i.e., divided by P(ρ)). Figure 6 illustrates an example of a weight image. The minimum weight is P²(ρ) = 0.83, hence the worst-case relative-sensitivity is $\sqrt{P^2(\rho)}=0.91$.

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Three quasars, spaced equally along the plane of the Galaxy, were used as secondary (phase) calibrators for the whole survey. Although initially assumed to be point-like, we found that each exhibited structure at the 0.1%–2.0% level, in the form of radio jets and nearby confusing sources. Using the full complement of data available we imaged and self-calibrated each secondary calibrator field out to the full-width half-power radius. The resulting clean-component models were used as inputs to the calibration procedure. Images of the secondary calibrators are presented in Figure 7. Quasars 1832−105 and 1856+061 deviate from point sources, exhibiting jets with flux densities peaking at 2% of the main peak. The source 1925+211 shows significant structure within 1 arcmin of the central source, including an elongated jet and two point sources of 1 mJy and 6 mJy (0.1% and 0.4% of the main peak, respectively).

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Two primary flux density calibrators were observed, providing a redundant means of flux-calibrating the data. 1331+305 (3C286) was observed at the beginning of an observing block and 0137+331 (3C48) at the end. With a 5 GHz flux density of 7.47 Jy 1331+305 was the preferred calibrator. However, 0137+331 (S_5 GHz = 5.48 Jy) was used if technical or weather-related problems affected the initial data from a block.

The small number of calibrators observed allowed us to check the consistency of our calibration with time. Figure 8 shows the percentage deviation from the median flux densities of the three secondary calibrators and the backup primary calibrator. Each point on the plot represents an 8 hr block of observations. Calibrator flux densities were measured directly from the image data by manually drawing a polygon around each quasar and summing the flux within the polygon. For the secondary calibrators the standard deviation in flux is 2.7%, and for the backup calibrators 8.9%, consistent with the accuracy of previous VLA surveys (e.g., Condon et al. 1998, who quote 3% at 1.4 GHz). No variation with time is seen, implying that the calibration is stable over the two observing seasons. The scatter in the backup calibrator is a more appropriate error to quote for snapshot imaging and is adopted as the formal amplitude calibration error for CORNISH data.

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All CORNISH observations are phase-referenced to one of the three secondary calibrators and hence adopt their positional uncertainties. The formal positional uncertainties may be found in the VLA calibrator manual³¹ and are <150 mas for 1832−105, <10 mas for 1856+061 and <2 mas for 1925+211.

The dual-snapshot observing scheme was designed to deliver the most circular synthesized beam possible, while allowing both snapshots to be taken within a single 8 hr observing block. To minimize the total range of synthesized beam shapes in the survey each field should ideally be observed at an equal ±3 hr hour-angle before and after its zenith position. Scheduling constraints meant that this was not achieved in practice and a compromise of 4 hr between snapshot images was implemented. Hoare et al. (2012) presents the parameters of the synthesized beams attained in the final images, which we briefly summarize here.

Within each observing block the beam elongation increases toward lower declinations, while the position angle varies by ∼60 deg. The distribution of beam minor-axes in the survey area separates into two distinct populations, with a small peak at 077 and a large peak at 12. The smaller peak stems from the low-declination fields observed using the BnA array configuration, while the larger one contains the majority of fields observed using the B array. In contrast, the distribution of major axes values is monolithic, with a median at 15 and a standard deviation of 032. Ninety-eight percent of fields have elongations less than 2 and 74% less than 1.5.

Based on these values, we chose to force a circular restoring beam of FWHM 15 because this greatly simplified the mosaicking operation and meant that the restoring beam shape was constant across every mosaicked image. The value 15 was chosen as the median value of the measured major-axes from all CORNISH fields. The degree of super-resolution is presented in Figure 9 and is less than 1.5 in 96% of fields. The restoring beam area is larger than the synthesized beam area for 8154 fields (87.2%) and is less than 1.5 times greater in 9343 fields (99.9%).

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Figure 10 presents an image of the rms noise over the full survey area, with each color-coded hexagon representing a field. The locations of H ii region complexes are prominent as clumps of high-noise fields located close to the mid-plane of the Galaxy. Away from such regions the noise level within individual scan-rows (scanning in R.A.) is relatively constant compared to the variation between rows, which is largely weather related. The observation area can be divided into two regions with noticeably different noise properties. At declinations greater than δ = 142, the median rms noise is significantly lower (rms_outer = 0.25 mJy beam⁻¹) than the remainder of the survey area (rms_inner = 0.35 mJy beam⁻¹). This outer CORNISH region corresponds directly to the epoch IIIb observations detailed in Table 1. From the 2007 season onward the VLA made extensive use of the upgraded JVLA antennas, which have more sensitive receivers. In addition, the weather conditions were better in the second season than in 2006, when observations were affected by electrical storms. Observations of approximately the inner 20 deg of the CORNISH area (δ < −105) also took place during the second season, corresponding to epochs II and IIIa. However, the rms noise level is similar to the 2006 season for a number of reasons. In particular, the inner CORNISH region is seen at relatively low elevations from the VLA site, requiring the telescope to peer through a greater path-length of atmosphere. Emission from the atmosphere causes an increase in system temperature decreasing the signal-to-noise ratio in the data. The epoch II observations utilized fewer JVLA antennas and, because the telescope was at the beginning of the VLA/JVLA transition, required extensive flagging to render the data usable. Figure 11 presents a histogram of the distribution of noise measurements, sampled on 2' scales, across the whole survey. The division between the inner and outer CORNISH regions is obvious. Both regions exhibit high-noise tails, corresponding to fields containing bright and extended emission.

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4.3.1. Spatial Scale of Noise

Interferometry data often exhibit non-Gaussian noise statistics, largely due to the nonlinear deconvolution process and poorly sampled uv coverage at large spatial scales. In regions with complex structures on scales greater than ∼14'' the emission is poorly constrained by uv coverage of the VLA B arrays. If only a few short baselines contain most of the flux a simple fringe pattern is produced on the sky. The flux is not evenly distributed but accumulates at specific spatial scales, depending on the sampling in uv space. The deconvolution algorithms used here also struggle to model this emission, resulting in some of the flux being scattered onto the surrounding sky (see Section 3.2). It is important to characterize this "ripple" noise pattern before attempting to search for real emission in the CORNISH data.

We have measured the noise characteristics of representative CORNISH data affected by a ripple. The region chosen was centered on α = 18^h09^m2196, δ = −20°19'349 and the rms noise was measured using both the standard deviation (STDEV) and median absolute deviation from the median (MADFM) statistics. For a data set X = x₁, x₂... x_i... x_n MADFM is given by

$$\begin{equation} {\rm \sigma = K\ median\,(|x_i - medianx(X)|)}, \end{equation} \tag{ 2 }$$

i.e., the median of the deviations from the median value. For a normal distribution MADFM is equivalent to the standard deviation using a scale factor K = 1.4826. The advantage of MADFM is that it is insensitive to the presence of outliers in the distribution and delivers a robust estimate of the true noise. Measurements were conducted using a range of aperture sizes, varying between 12'' and 240'' in steps of 282. In total twenty one positions were measured, offset in declination by 6'' along a line centered on the noise peak. The scatter in the results (expressed in standard deviations σ) for each aperture size is plotted in Figure 12. From the plot we see that the scatter in the ensemble set of measurements increases as the aperture size decreases. The MADFM statistic remains stable at smaller spatial scales than the STDEV. At scales less than 2' the scatter in the STDEV measurement slowly rises, compared to MADFM, whose scatter remains less than 0.1 mJy beam⁻¹ until scales of 40''. Measurements of the global noise-properties of the CORNISH data are therefore best performed using apertures spanning 40'' or larger using the MADFM statistic.

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Tiles were automatically searched for emission using a custom procedure based on the OBIT FndSou task. FndSou identifies contiguous islands of emission above a global intensity threshold and attempts to fit one or more 2D Gaussians to each. This approach works well in the simplest case of an image with homogeneous noise properties, however, in the worst-case scenario the rms noise can change by a factor of a few over a 20' × 20' tile. This is especially true of tiles covering the Galactic mid-plane, where massive star-forming complexes are common. Using a single intensity threshold often results in spurious detections or omissions of real sources. To compensate for variable noise levels we ran FndSou on a 9 × 9 grid of "patches" within the tile area. Each patch is 800 pixels (4') on a side and overlaps adjacent patches by 400 pixels in R.A. and decl. The local rms noise in each patch was determined using a histogram analysis clipped at 3σ from the median value. With this patch layout a radio source within a 2' band around the tile edge may be detected in two patches, except at the tile corners. A source in the interior may be detected in up to four overlapping patches. The maximum fitted Gaussian FWHM was constrained to be ⩽30'' in keeping with the uv coverage. Fits within 14'' of the patch edge were deemed invalid, except where a patch abutted a tile edge. Running this patch-based emission finding procedure results in a degenerate list of sources with coincident positions derived from overlapping patches. A list of unique Gaussian fits to each tile was produced by searching for duplicates at similar positions (separation <1'') and with similar peak amplitudes (A_min/A_max > 0.7). The Gaussian fit closest to the center of a patch was retained.

Initially, the search was conducted using a 4σ local noise threshold and aperture photometry was performed to weed out detections with a signal-to-noise ratio σ < 5.0 (σ = maximum pixel/rms noise). An elliptical aperture was used to measure the source properties, which extended to the 3σ Gaussian major and minor axes (2.548 ×FWHM). If the emission was indeed Gaussian in shape this aperture would encompass 99.7% of the emitting flux. The rms noise and median background level of the sky were measured from a 20'' wide annulus centered on the source and offset from the measurement aperture by 5''. The annulus width was chosen to sample the local noise pattern without being influenced by ripples or negative bowls (see Section 4.3.1). In crowded regions the sky annulus is likely to contain bright and real sources so the noise was measured using the robust MADFM statistic. The parameters of the valid Gaussian fits and photometric measurements were both recorded to the MySQL database, although the Gaussian fits are preferentially used in the default CORNISH catalog.

The source finder determines accurate fluxes for isolated and unresolved sources but decomposes complex structures into multiple overlapping Gaussians fits. It is highly desirable to merge these into a single measurement to avoid over-interpreting the number counts and properties of sources in the final catalog. Clusters of Gaussians were identified in the catalog using a friends-of-friends search: a Gaussian was associated with a cluster if it was within 12'' of any other member. In total, 741 clusters were found and these were all inspected manually. To distinguish between adjacent but unrelated sources and over-resolved emission the morphology at 5 GHz was compared to that in the Spitzer GLIMPSE mid-infrared images. The most common extended sources in the images are UCH ii regions and planetary nebulae, each of which have distinctive mid-infrared signatures. For these types of object the morphology of the 8 μm emission often echos that of the radio continuum (Hoare et al. 2007). If a cluster of Gaussian fits was found to trace an over-resolved source then the fitted parameters were replaced with a single measurement under a polygonal aperture manually drawn around the emission. Figure 13 shows an example of a polygon carefully drawn around the border of a cometary H ii region. The flux density is calculated from the sum of the pixels within the source aperture minus the median background level in the vicinity of the source. In addition to the coordinates of the peak emission (for which the source is named in l and b) we also record the geometric and intensity-weighted positions.

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Below we explain how the properties of the sources were measured and the uncertainties calculated. The final values are presented in the CORNISH catalog, including the measurement-error and the absolute uncertainty on each parameter, incorporating the calibration error of 8.9%.

5.3.1. Gaussian Fits

Uncertainties on the Gaussian fits are calculated using the equations derived by Condon (1997), summarized here for convenience. Noise in interferometric data is correlated on the scale of the synthesized beam FWHM, in this case θ_bm = 15. The effective signal-to-noise level ρ of a source with measured peak amplitude A_peak seen against a background of correlated Gaussian noise is given by

$$\begin{equation} \rho ^2=\frac{\theta _{\rm M}\theta _{\rm m}}{4\theta _{\rm bm}^2} \left[1+\left(\frac{\theta _{\rm bm}}{\theta _{\rm M}}\right)^2\right]^{\alpha _{\rm M}} \left[1+\left(\frac{\theta _{\rm bm}}{\theta _{\rm m}}\right)^2\right]^{\alpha _{\rm m}} \frac{A_{\rm peak}^2}{\sigma _{\rm sky}^2}, \end{equation} \tag{ 3 }$$

where θ_M and θ_m are the respective major and minor fitted axes and σ_sky is the rms noise measured directly from the image. The exponents α_M and α_m have been estimated by Condon (1997) via Monte Carlo simulations and are α_M = α_m = 3/2 for the amplitude and flux density errors, α_M = 5/2, α_m = 1/2 for the error on the major axis, and α_M = 1/2, α_m = 5/2 for the minor axes, position angle and absolute coordinate errors. On average, the signal-to-noise ratio is increased by a factor of 1.4. The positional uncertainties parallel to the major (σ_M) and minor (σ_m) fitted axes are given by

$$\begin{equation} \frac{\sigma _{\rm M}^2}{\theta _{\rm M}^2} = \frac{\sigma _{\rm m}^2}{\theta _{\rm m}^2} \approx \frac{1}{(2 \ln \,2)\rho ^2}, \end{equation} \tag{ 4 }$$

using values for ρ calculated from Equation (3). When the fit is projected onto equatorial axes the absolute position errors in right ascension (σ_α) and declination (σ_δ) become

$$\begin{equation} \begin{array}{@{}l}\sigma _{\alpha }^2\approx \epsilon _{\alpha }^2+ \sigma _{\rm M}^2\,{\rm sin^2(P.A.)} +\sigma _{\rm m}^2\,{\rm cos^2(P.A.)}, \\ \ms \ms \sigma _{\delta }^2\approx \epsilon _{\delta }^2+ \sigma _{\rm M}^2\,{\rm cos^2(P.A.)} +\sigma _{\rm m}^2\,{\rm sin^2(P.A.)}, \end{array} \end{equation} \tag{ 5 }$$

where P.A. is the position angle of the fitted major axis east of north and _α = _δ ≈ 0.1 arcsec is the systematic positional uncertainty. This value was determined via a comparison between CORNISH and catalogs of quasars whose positions are determined to milliarscsecond accuracy. The 15 matching quasars were drawn from the Goddard VLBI astrometric catalogs,³² Very Long Baseline Array Galactic Plane Survey (VGaPS; Petrov et al. 2011) and the VLA calibrator manual³³ and their median offset of 0.1 arcsec was adopted as the systematic positional uncertainty for CORNISH.

Errors in the P.A. may be calculated from

$$\begin{equation} \sigma _{\rm PA}^2=\frac{4}{\rho ^2}\left(\frac{\theta _{\rm M}\theta _{\rm m}}{\theta _{\rm M}^2-\theta _{\rm m}^2}\right)^2, \end{equation} \tag{ 6 }$$

although we note that position angle values are only relevant when one or more axes is significantly resolved. Uncertainties associated with the fitted major and minor Gaussian FWHM are given by

$$\begin{equation} \sigma ^2(\theta _{\rm M})=\frac{2\theta _{\rm M}^2}{\rho ^2} + \epsilon _{\theta }^2\theta _{\rm M}^2 \; {\rm and} \; \sigma ^2(\theta _{\rm m})=\frac{2\theta _{\rm m}^2}{\rho ^2}+ \epsilon _{\theta }^2\theta _{\rm m}^2. \end{equation} \tag{ 7 }$$

The fractional calibration uncertainty _θ = 0.02 is adopted from the VLA NVSS survey (Condon et al. 1998), which was observed using a similar snapshot mode. A single characteristic measured angular size θ_f may be obtained from the geometric mean of the major and minor axes

$$\begin{equation} \theta _{\rm f} = \sqrt{\theta _{\rm M}\,\theta _{\rm m}}, \end{equation} \tag{ 8 }$$

and its associated measurement uncertainty given by

$$\begin{equation} \sigma (\theta _{\rm f}) = \frac{\theta _{\rm f}}{2}\sqrt{\frac{\sigma ^2(\theta _{\rm M})}{\theta ^2_{\rm M}} + \frac{\sigma ^2(\theta _{\rm m})}{\theta ^2_{\rm m}}}. \end{equation} \tag{ 9 }$$

The restoring beam was forced to be a circular Gaussian of FWHM θ_bm = 15 over the whole survey area so the deconvolved source size θ_s in arcseconds may be found from

$$\begin{equation} \theta _{\rm s} = \sqrt{\theta _{\rm f}^2 - 1.5^2}, \end{equation} \tag{ 10 }$$

although we note that detections with θ_f < 18 are considered unresolved in CORNISH.

The fitted amplitude A_peak must be corrected for the clean bias ΔA_cb = − 0.94 σ_sky, which we measured in Section 5.6 below, so

$$\begin{equation} A = A_{\rm peak} - \Delta A_{\rm cb}. \end{equation} \tag{ 11 }$$

The uncertainty on the fitted amplitude may be calculated from

$$\begin{equation} \sigma _A^2=\frac{2A^2}{\rho ^2} + \epsilon _A^2 A^2, \end{equation} \tag{ 12 }$$

where _A = 0.089 is the fractional amplitude calibration error. Finally, the integrated flux density S under a 2D Gaussian is given by

$$\begin{equation} S =\frac{A\,\pi }{4\,\ln (2)}\,\frac{\theta _{\rm M}\,\theta _{\rm m}}{\theta _{\rm bm}^2} \end{equation} \tag{ 13 }$$

and the corresponding uncertainty is

$$\begin{equation} \frac{\sigma _S^2}{S^2}\approx \frac{\sigma _A^2}{A^2}+\frac{\theta _{\rm bm}^2}{\theta _{\rm M}\theta _{\rm m}}\, \left[\frac{\sigma ^2(\theta _{\rm M})}{\theta _{\rm M}^2}+\frac{\sigma ^2(\theta _{\rm m})}{\theta _{\rm m}^2}\right]. \end{equation} \tag{ 14 }$$

5.3.2. Aperture Photometry

The peak amplitude reported for sources measured using aperture photometry is simply the intensity of the brightest pixel within the source aperture, corrected for the clean bias.

$$\begin{equation} A = A_{\rm max} -\Delta A_{cb}. \end{equation} \tag{ 15 }$$

The effective signal-to-noise of the source in the presence of correlated Gaussian noise may be determined from a modified version of Equation (3) in which θ_M and θ_m are both replaced with the intensity-weighted diameter

$$\begin{equation} \theta _{\rm d} = 2 \left. \displaystyle \sum \limits _{i=1}^{N_{\rm src}} r_i A_i \left /\right. \displaystyle \sum \limits _{i=1}^{N_{\rm src}} A_i. \right. \end{equation} \tag{ 16 }$$

For a perfectly circular Gaussian source θ_d ≡ θ_f ≡ FWHM. In Equation (16) r_i is the angular distance from the ith pixel to the brightness-weighted center and A_i is its intensity. The sky noise corrected for Gaussian correlation is then

$$\begin{equation} \frac{1}{\sigma _{\rm g}^2} = \frac{\theta _{\rm d}^2}{4\theta _{\rm bm}^2} \left[1+\left(\frac{\theta _{\rm bm}}{\theta _{\rm d}}\right)^2\right]^{\alpha }\frac{1}{\sigma _{\rm sky}^2}, \end{equation} \tag{ 17 }$$

where α = 3 for all errors. The uncertainty on the peak amplitude is

$$\begin{equation} \sigma _A^2 = \sigma _{\rm g}^2 + \epsilon _A^2 A_{\rm max}^2, \end{equation} \tag{ 18 }$$

where _A = 0.089 is the calibration error.

The equation for measuring the integrated flux density S_phot of a source using aperture photometry can be written as

$$\begin{equation} S_{\rm phot}=\left(\sum \limits _{i=1}^{N_{\rm src}} A_i - N_{\rm src}\,\bar{B}\right) \left /\right. a_{\rm bm}, \end{equation} \tag{ 19 }$$

where ∑Nsrci = 1Ai is the total flux in the source aperture summed over Nsrc pixel elements (in units of Jy pixel−1), $\bar{B}$ is the background flux density estimated from the median level in the sky-annulus and abm = π θ2bm/(4 ln (2) Δ2pix) is the beam-area in pixels (28.33 pixels for CORNISH data). Sphot must also be corrected for clean-bias. If the source is unresolved the missing flux ΔScb is given by

$$\begin{equation} \Delta S_{\rm cb} = -0.94\,\sigma _{\rm sky}\,\frac{\pi }{4\,\ln (2)}\,\frac{\theta _{\rm bm}^2}{a_{\rm bm}}, \end{equation} \tag{ 20 }$$

however, because the clean-bias reduces the flux in all clean-components (CCs) by a constant factor the effect on extended emission is difficult to gauge. The minimum number of CCs required to model an extended source can be estimated from the number of beam-areas n_beams subtended by the emission. The integrated flux density is then

$$\begin{equation} S = S_{\rm phot} - \Delta S_{\rm cb} * n_{\rm beams}. \end{equation} \tag{ 21 }$$

The error on the integrated flux density may be found from

$$\begin{equation} \sigma _S^2 = \left.\left(\sigma ({\textstyle \sum } A_i)^2 + \frac{\pi \,N_{\rm src}^2\,\sigma _{\rm g}^2}{2\,N_{\rm sky}}\right) \left /\right. a_{\rm bm}^2\right., \end{equation} \tag{ 22 }$$

where σ²_g is the corrected variance, N_src and N_sky are the number of pixels in the source- and sky-apertures, respectively. The term $\sigma ({\textstyle \sum } A_i)$ is the uncertainty on the sum over the pixels in the source aperture given by

$$\begin{equation} \sigma ({\textstyle \sum } A_i) = \sum \limits _{i=1}^{N_{\rm src}} \left(\sqrt{\sigma _{\rm g}^2 + \epsilon _A^2 A_i^2 } \right). \end{equation} \tag{ 23 }$$

The uncertainty on the intensity weighted diameter maybe found from

$$\begin{equation} \frac{\sigma ^2(\theta _{\rm d})}{\theta _{\rm d}^2} = \frac{\sigma _{\rm g}^2\,\sum r_i^2}{(\sum r_i\,A_i)^2} + \frac{N_{\rm src}\,\sigma _{\rm g}^2}{(\sum A_i)^2}, \end{equation} \tag{ 24 }$$

where the sums are taken over the pixels in the source-aperture. For emission measured using a polygonal aperture the intensity-weighted position is given by

$$\begin{equation} \bar{x} = \left. \displaystyle \sum \limits _{i=1}^{N_{\rm src}} x_i A_i \left /\right. \displaystyle \sum \limits _{i=1}^{N_{\rm src}} A_i. \right., \end{equation} \tag{ 25 }$$

where x_i is the right ascension (α) or declination (δ) in the ith pixel. The corresponding error in $\bar{x}$ is given by

$$\begin{equation} \sigma x^2 = \left[\,\frac{\sigma _{\rm g}^2\,\sum x_i^2}{(\sum x_i\,A_i)^2} + \frac{N_{\rm src}\,\sigma _{\rm g}^2}{(\sum A_i)^2}\,\right]\,\bar{x}^2 + \epsilon _x^2, \end{equation} \tag{ 26 }$$

with _x = _α = _δ, the absolute positional error of the associated phase calibrator.

We have attempted to estimate the number of spurious sources detected in well calibrated and well behaved data by running the source finder on inverted tiles, i.e., tiles where the pixel values have been multiplied by −1. Any negative detections will be false and allow us to estimate the number of spurious sources as a function of the signal-to-noise ratio. Fourteen tiles were selected to be representative of the emission properties across the survey region. They contained variously: no strong emission, one or more point sources with S_5 GHz > 50 mJy, weak extended emission, and bright extended sources causing moderately elevated noise levels (0.5 mJy < rms < 0.8 mJy). For comparison, we ran the source finder using a 3.5σ cutoff on both the inverted and the regular tiles. Figure 14 plots the cumulative counts of detections as above a signal-to-noise ratio, expressed as σ. The gray shaded histogram illustrates the number of spurious detections in the inverted tiles, while the hatched histogram illustrates the detections in the normal tiles, some of which will be real. Note that below 4.5σ the detections are dominated by spurious sources. The 14 tiles represent 1.23% of the survey area, so by scaling the plot by 81 we can estimate the total distribution of spurious sources in CORNISH. In Figure 14 the number of spurious sources found decreases to 81 at 6.1σ, above which our scaling is too crude to sample. For populations governed by Gaussian statistics the fraction f(σ) of the populations lying within a σ-threshold is given by

$$\begin{equation} f(\sigma) = 1 - erf(\sigma /\sqrt{2}), \end{equation} \tag{ 27 }$$

where erf(σ) is the Gaussian error function. The solid black curve in Figure 14 plots f(σ) assuming the total number of possible detections is equal to the number of synthesized beam areas in CORNISH (≈5.6 × 10⁸ beams). It is clear that this assumption underestimates the number of spurious sources found, so we fit f(σ) with the total number of sources as a free parameter. The fit is shown by the short-dashed line and is dominated by the large number of sources in the bins with σ < 4. Above 4σ the distribution has a shallower fall-off than expected for Gaussian statistics. An alternative is shown by the long-dashed line, which is fitted only to the bins with σ > 4 and uses the error-function of a distribution with a narrower width than purely Gaussian (σ = 0.9σ_gauss). It is a significantly better match to the high signal-to-noise end of the distribution and predicts less than one spurious source above 7σ. Based on this reasoning we have chosen a signal-to-noise cutoff of 7σ for a high-reliability CORNISH catalog. We caution that data with greater complexity or poor calibration may introduce significant numbers of false sources above this level, so this does not mitigate the need to manually inspect the data for artifacts. Sources detected below 7σ are not offered as an official data-product, but this low-reliability catalog will be made available on the CORNISH web page

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5.4.1. Density of Low Signal-to-noise Sources

A density map of the CORNISH detections serves to highlight regions containing excessive numbers of weak sources, some of which may be spurious. Figure 15 plots the number density of CORNISH sources above 5σ summed within an 8 arcmin radius. The most prominent feature is a line of elevated pixels corresponding to the scan row at δ = −16°55'0313 (133 < l < 145). This row is unique in the survey as each field has only a single 40 s snapshot observation. Data in the second pass were found to be corrupted and no repeat observations were scheduled due to an array configuration change.

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Isolated clumps of pixels with high source counts (e.g., at l = 12, l = 31, l = 43 and l = 49) correspond to molecular cloud complexes forming massive stars. The over-density of weak sources in these regions could be due to either a real increase in source counts or to an increase spurious in sources generated by the deconvolution process. Both scenarios warrant careful inspection of the data.

To quantify the formal sensitivity limits of the pipeline reduced data we conducted completeness tests on tiles from "empty" parts of the sky. The tiles were chosen to have few detections above 5σ, homogeneous noise properties and be free of obvious imaging artifacts. One-hundred artificial point sources were injected into the calibrated uv data for each tile before creating a mosaicked image. The flux densities of the injected sources were varied randomly between 0.5 and 5.0 mJy, so as to bracket the expected sensitivity limit. Positions were also chosen randomly, but avoided known emission, the tile edges or regions where noise spikes were common. FndSou was then used to find and fit the emission with Gaussians. After 20 iterations of the injection-imaging-fitting routine the aggregate results were compared to the injected source parameters.

Figure 16 plots the percentage of sources recovered as a function of the flux density for tiles covering a range of rms values and epochs. The image parameters are presented in Table 2 alongside the 50% and 90% completeness limits. As expected, tiles with lower measured rms noise levels tend to have lower completeness limits. There is, however, significant variation between tiles as the local completeness limit ultimately depends on the uniformity of the noise pattern within the tile. At worst (tile 529, epoch I) the CORNISH survey is 90% complete to point sources at the 3.9 mJy level.

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When deconvolving the synthesized beam from the images, the flux level at which the CLEAN algorithm halts must be chosen carefully. If the cutoff is set too far above the noise then the residual images will be dominated by sidelobe patterns. If it is too low CLEAN will inadvertently identify noise spikes and sidelobes as real emission. Both negative and positive clean components on sidelobes will result in flux being subtracted from the positions of real sources and can artificially lower the rms noise. The AutoWindow function described in Section 3.2 has been shown to reduce the clean bias. However, we chose a relatively low CLEAN cutoff when imaging CORNISH data and need to measure the bias level in order to evaluate the correct flux-densities and uncertainties for the CORNISH catalog. In a similar test to the one presented in Section 5.5 we inserted 20 point sources into the uv data for each of tiles 43, 273, 529, and 1248. The flux densities were set randomly between 2 and 20 mJy. The data were imaged and mosaicked using the CORNISH pipeline, and aperture photometry was used to recover the artificial source fluxes. We found that the clean bias was consistent across all four tiles, despite being representative of different epochs. We adopt a mean clean bias of ΔA_peak = −0.94 σ_sky (typically 0.33 mJy), indicating a moderate level of over-cleaning compared to NVSS, which quotes ΔA_peak = −0.67σ. We judge that this will not affect the utility of the catalog.

According to the results of Section 5.4 we do not expect to find significant numbers of spurious sources above 7σ in well-behaved CORNISH data. This statement is not necessarily true of high-noise fields containing bright and extended emission associated with massive star-forming complexes. Occasionally, peaks in the rippled noise pattern may be mistaken for real emission, or calibration errors may conspire to create false sources. To alleviate this problem we visually inspected all high-reliability CORNISH detections (i.e., those peaking above 7σ) to assess them as potential artifacts.

The CORNISH team visually inspected all mosaic tiles and individual sources in the 7σ catalog. All sources were classified as being either "unlikely," "possibly" or "likely" an artifact based on the criteria above, i.e., located on a peak in a high noise ripple region, near a very bright source, or in a region where there appears to be an excessive number of potentially spurious 5σ–7σ sources (see Figure 15). If a source suspected of being possibly or likely an artifact was found to have a radio or infrared counterpart then, of course, the flag was left as "unlikely" in the CORNISH database. Smaller UCH ii regions lying within the noise radius of a much brighter emission often have counterparts in the GLIMPSE IRAC data, while planetary nebulae (PNs) are often seen in the UKIDSS data, confirming them as real detections. Both source types appear in the far-infrared MIPSGAL bands (24 μm and 70 μm). Real extragalactic sources are not likely have counterparts in the infrared data sets and so retain their possible-artifact flag in suspect regions.

Isolated and unresolved sources identified by the source finder have two recorded entries taken from fitted Gaussian parameters and aperture photometry measurements. Sources exhibiting structured and extended emission have a single entry, based on aperture photometry performed using a manually drawn polygon. When assembling an aggregate catalog we favored the Gaussian fitted values. The photometric measurements are useful for diagnostic purposes.

An excerpt from the final CORNISH catalog is presented in Table 3. The columns are as follows. Column 1 contains the CORNISH source name, constructed from the Galactic longitude and latitude of the source. The equivalent right ascension (α) and declination (δ) are displayed in Columns 2 and 3, respectively. For sources well fitted by Gaussians the adopted coordinates are simply the peak positions of the fits. The intensity-weighted position is quoted for extended sources measured using a polygonal aperture. The associated positional uncertainties are given in Columns 4 and 5. Two uncertainty values are quoted for catalog entries. The first value is the absolute uncertainty, incorporating both measurement and calibration errors. The second value (in brackets) is the error associated with the photometry or Gaussian fit alone. Column 6 presents the peak flux density in units of mJy beam⁻¹. The 5 GHz integrated flux density (S_5 GHz) is presented in Column 7. Column 8 contains the measured angular scale of the emission θ_f, which has been determined from the geometric average of the major and minor Gaussian fit axes, or the intensity-weighted diameter in the case of extended emission. Sources with θ_s > 1.8 are considered to be resolved in the CORNISH images and their deconvolved sizes are presented in Column 9. The local rms noise measured from the photometric sky-annulus is recorded in Column 10. Column 11 notes how the flux density was measured, either with an polygonal aperture, or using the Gaussian fit. Finally, Column 12 contains a range of flags notifying the reader if the source

• 1.  
  is within 12'' of another source,
• 2.  
  is lying on an unusually high noise region (rms >0.45 mJy beam⁻¹),
• 3.  
  is imaged using the smoothed weighting scheme described in Section 3.2.2 (within 445 of a field center, i.e., half a primary beam FWHM),
• 4.  
  is within 3' of a bright (>0.5 Jy) source,
• 5.  
  is within 2' of the edge of the survey,
• 6.  
  is within an area containing numerous low signal-to-noise detections likely to be spurious,
• 7.  
  overlaps with another source,
• 8.  
  is flagged as a suspected artifact during manual inspection.

The flags are described in more detail in the footnotes to Table 3.

The full CORNISH catalog is offered to the astronomical community as a plain text file or VO table (XML-based table format defined by the Virtual Observatory) on the project Web site (http://cornish.leeds.ac.uk). A query-based Web interface is also available which allows the user to retrieve specific catalog subsets and drill down to the underlying data.

6.2.1. Angular Size

The measured angular size θ_f quoted in the catalog is given by the geometric average of the major and minor fitted Gaussian FWHM axes ($\sqrt{\theta _{\rm M}\,\theta _{\rm m}}$), or by the intensity-weighted diameter (θ_d) in the case of emission measured using a polygonal aperture. For a bright source with a Gaussian morphology these measurements are equivalent within the errors. Figure 17 plots the distribution of measured angular sizes for the high-reliability catalog. The distribution begins to flatten at size scales greater than 5 arcsec, before tapering out at 30'', where the upper limit for the source-fitter is set. Above sizes of 14'' the deconvolution algorithm struggles to model the poorly sampled longer uv-spacings (see Section 3.2.2), hence the slight increase in counts at that scale—broad sources are artificially truncated at sizes of 14''.

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The uncertainty on the angular size of CORNISH sources is better than 03 for 96% of compact (θ_f < 5'') catalog entries. We consider sources with θ_f < 18 (i.e., the restoring beam size plus 03) to be unresolved. Sixty-one percent of the 7σ catalog fall into this category. Below we examine the differences between the resolved and unresolved populations.

6.2.2. Galactic Distribution

Figure 18 illustrates the distribution of CORNISH sources as a function of Galactic latitude (upper panel) and longitude (lower panel). The solid-shaded histogram contains all sources in the high-reliability catalog, while the hatched histogram contains only the subset of resolved detections (859 sources). Resolved sources account entirely for the broad peak seen in the latitude distribution, with the remaining unresolved detections exhibiting a flat profile. The scale-height of the resolved latitude distribution is 047, consistent with that of UCH ii regions, 6.7 GHz methanol masers and other tracers of high-mass star formation (e.g., Green et al. 2009; Urquhart et al. 2007, 2009, 2011). The supposition that a large fraction of the resolved sources arise in high-mass star-forming regions is lent weight by their Galactic longitude distribution. The number of sources per 2° bin increases gradually toward longitude zero, while the two spikes at l ≈ 43° and l ≈ 50° correspond to the W49 and W51 complexes, respectively. Conversely, unresolved detections (1719 sources) show a flat distribution with Galactic longitude and are likely to contain significant numbers of AGNs, and other extragalactic sources. We note that this is partly by design as course adjustments were made to the deconvolution algorithm in order to keep the number of low-level sources roughly constant (Figure 3). The expected density of extragalactic sources in CORNISH may be calculated from Equation (A2) of Anglada et al. (1998), using the 5 GHz source counts of Condon (1984). Assuming median values of rms noise for the two regions presented in Figure 11, we expect to find ∼2400 extragalactic sources in our 7σ catalog, consistent with the number of unresolved detections. Most extragalactic sources can be classified as they are not detected in any of the infrared wavebands. Specific catalogs of CORNISH sources identified as UCH ii regions, PNs and AGNs will be presented in a forthcoming paper.

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6.2.3. Flux Density and Peak Flux

Figure 19 shows the distributions of flux densities and peak fluxes for the CORNISH sources. At flux density levels of ∼5 mJy or greater the distribution is well fitted with a power law of index −0.81. Below 3 mJy the number of sources begins to decrease as the 7σ detection limit is encountered. The distribution of flux densities for resolved sources turns over at approximately 6 mJy due to the constraints imposed by their selection and the 7σ signal-to-noise cutoff.

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The peak flux distributions are identical for the high-reliability and the resolved source catalogs. Above the sensitivity cutoff (∼2.5 mJy beam⁻¹), both are fit by a power law of n_src∝S^−0.91_5 GHz.

Of all prior observations the White et al. (2005) VLA survey of the Galactic plane has the most similar observing setup and sky-coverage. A comparison to that work serves as a useful sanity-check on the ensemble properties of the CORNISH catalog and images. The 5 GHz component of the White et al. survey was observed using the D, DnC, and C arrays. These more compact VLA configurations (compared to B and BnA) yield better sensitivity to extended emission than CORNISH, but at a lower resolution (∼6''). White et al. (2005) imaged the Galactic plane between −10° < l < 42° and |b| < 04, of which 25.6 deg² overlap with the CORNISH target area. The measured noise properties of their images are lower, with a median rms of ∼0.27 mJy beam⁻¹ compared to ∼0.35 mJy beam⁻¹ for CORNISH data. The cutoff limit for the White et al. source catalog was chosen to be 5.5σ (∼1.4 mJy beam⁻¹) compared to 7σ (∼2.5 mJy beam⁻¹) in this work. We would expect similar flux densities for compact sources (⩽6'', see Figure 4) common to both catalogs, despite differences in uv coverage. Systematic errors present in either catalog should be obvious in a flux–flux comparison plot.

The White et al. 5 GHz catalog contains 1822 entries in the overlapping area and we match 558 of these with 521 CORNISH sources using a 5'' search radius. The number of matches diminishes significantly at matching radii greater than 2'', however, a 5'' matching radius was chosen to allow for offsets in the positions assigned to resolved sources in both catalogs. Figure 20 presents a comparison of the measured flux densities for sources successfully cross-matched between the two surveys. The measurements agree, on average, to within 39%, with no evidence of systematic differences. A greater fraction of sources in the high flux density bins are resolved, hence the outliers in the plot above ∼100 mJy may be attributed to imaging and measurement differences between the two surveys. Points representing individual sources in Figure 20 are color-coded to indicate angular size in the CORNISH catalog. Unresolved sources (blue) cluster around the equality line while the outliers are almost all extended (red).

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In total, 1264 sources in the White et al. catalog remain unmatched using a simple cone search within 5 arcsec. Of these, 50% lie above our 7σ sensitivity threshold and are sufficiently bright to have been detected in CORNISH. The reasons for the disparity become apparent upon comparing the White et al. and CORNISH images. A significant fraction of the bright, unmatched sources have angular scales greater than ∼20'' in the White et al. data and are simply resolved out by the VLA B configurations used by CORNISH. Figure 21 (panels (i) and (ii)) presents examples of such objects. The central source powering the radio galaxy shown in panel (i) is detected in both surveys as a 25 mJy point source. The radio lobes have angular scales of ∼30'' in the White et al. image and the brighter southern lobe has a peak flux density of 14 mJy beam⁻¹. In the corresponding CORNISH image the northern lobe is completely resolved out, while the southern lobe is detected at a 5.5σ level and is therefore not included in the high-reliability catalog. Similarly, the cometary H ii region G24.799+0.097, shown in panel (ii), is resolved into multiple components by CORNISH. When assembling the CORNISH catalog we took great care to identify such over-resolved emission as a single source (see Section 5.2). In the White et al. catalog individual Gaussian fits to complex emission are left separate. G24.799+0.097, for example, has four catalog entries and a 15'' matching radius is required to correctly match these to their CORNISH counterpart.

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Panel (iii) of Figure 21 presents an image of the G34.26+0.15 star formation region. G34.26+0.15 is divided into three components (a, b, and c), of which c is the prototype cometary UCH ii region (van Buren et al. 1990). The CORNISH image clearly resolves all three components with excellent image fidelity. By contrast the White et al. image barely resolves the c component and contains significant numbers of imaging artifacts.

The remaining unmatched White et al. sources derive from intrinsic differences in the image quality between the two surveys. In general, the CORNISH images have more homogeneous noise properties and are of higher quality. The White et al. images contain numerous compact sites of emission not present in the equivalent CORNISH mosaics. These often occur in regions adjacent to very bright sources or poorly imaged extended emission, and are themselves generally unresolved and weak (90% <10 mJy beam⁻¹). Their morphology and location makes them likely to be artifacts of the imaging process. Examples of such artifacts are visible in panel (iii) and, to a lesser extent in panel (ii) of Figure 21. Artifacts in the CORNISH image are limited to a moderate level of ripple and few noise spikes. By comparison the White et al. images often contain significant sidelobe structure and a number of spurious emission sources. The advantages of utilizing a semi-automatic pipeline are apparent in the high-quality of the CORNISH images. In general, such fields also illustrate the importance of manually inspecting the results of automatic source-finders when dealing with under-sampled interferometric data.

There are 262 CORNISH sources which have no counterpart in the White et al. catalog, despite peaking well above the nominal 5.5σ detection limit. A small fraction of these are pathological cases, like the two compact sources at the center of the CORNISH image in panel (ii) of Figure 21. The sources have flux densities of 12 and 20 mJy and should be visible in the White et al. image. A linear discontinuity cuts through the image at this position, so we speculate that the omission derives from a problem with the imaging process used by White et al. (2005). The majority of the remaining unmatched CORNISH sources are detected below the 10σ level and when present in the White et al. images are washed out by ripples in the noise or other imaging artifacts.