Continuing the theme of automated procedures for COBRaS (and indeed other radio surveys with exceptional data volumes), the detection of sources in the radio maps begins the final stage of the pipeline. The ideal source finder is one which is complete, finding all the sources in the image, and one with a low false-positive ratio i.e. finding only real sources. Due to the importance of source detection, a number of algorithms have been developed over the years using a multitude of techniques.
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2MASS Catalogue
2MASS OB Candidate Catalogue
Figure 4.6: Same plot as Figure 4.4, J–H versus H–K magnitude colour plot with the 2MASS catalogue (red) and the 2MASS OB candidate catalogue (blue). The same regres- sion line and loci from the OB catalogue is used to determine the 2MASS OB candidate catalogue.
A recent review of source detection methods was conducted by Masias et al. (2012), with discussions on basic image transformation, Bayesian inference, matched filtering, multi-scale approaches and wavelet transformations. These cover a range of objectives, such as source detection, faint source detection, point source detection, extended source detection and a range of observing bands from X-ray to radio. Recent developments in radio source detection algorithms for the Australian SKA Pathfinder (ASKAP) Evolution- ary Map of the Universe (EMU) project have produced two codes: AEGEAN (Hancock et al. 2012), and BLOBCAT (Hales et al. 2012).
AEGEAN is a radio point source detection algorithm which adopts the floodfill algo- rithm to detect sources and fit Gaussian point spread functions to the sources, otherwise known as island of pixels. It assumes all sources are unresolved or point-like and convolves the image with a Lagrangian matrix to produce a covariance map. This breaks the islands
into ‘summits’ which signify the peak presence of a point source. Multiple summits within an island represent multiple blended point sources, and a Gaussian is fitted to each. The consequent flux extraction will account for multiple blended Gaussian sources.
BLOBCAT is a radio source detection algorithm which also adopts the floodfill al- gorithm to detect sources (Hales et al. 2012). It however does not assume the source is unresolved and the flux extraction performs both Gaussian and non-Gaussian routines to calculate the flux. For resolved sources, it performs a number of corrections for the peak surface brightness bias, where a Gaussian may over or under estimate the peak flux de- pending on the nature of the source (see Figure 3 of Hales et al. 2012), and the integrated surface brightness bias, where the floodfill algorithm does not fill the entire source volume before the cut off threshold.
Both algorithms use the same floodfill detection method (discussed below) and demon- strate a high completeness; 93.87% at 5σ, and reliability; 98.69% at 5σ (Hancock et al. 2012). Since COBRaS is also primarily a point source radio survey, this algorithm is desirable for its proven robustness and automated nature.
4.2.1 Floodfill Algorithm
The floodfill algorithm operates on an image with two inputs; the seed or detection thresh- old (Ts) and the flood threshold (Tf). If a pixel in the image is above the seed threshold
Ts, then an island is grown around the seed pixel by testing whether adjacent pixels are
above the flood threshold Tf. All pixels that fit the flood threshold criteria are added to
the island and the next adjacent pixels are considered. This iterative procedure continues until all adjacent pixels to the island are below the flood threshold.
In the AEGEAN algorithm (Hancock et al. 2012) the seed threshold Ts is set to 5σ (5
times the noise level) and the flood threshold Tf = 4σ. This level of flooding is sufficient
to characterise the islands and fit Gaussians to the components. BLOBCAT (Hales et al. 2012) is also seeded at Ts= 5, but the flood threshold Tf= 2.6, because the authors found
that this enabled true source pixels to be flooded whilst avoiding flooding non-source pixels and perform a volume bias correction to account for any unfilled source pixels.
4.2.2 Modifications to the Floodfill Algorithm
It is evident that the floodfill algorithm is an effective source detection method. However, these algorithms have only been tested for 5σ sources, enbedded in Gaussian noise. To
arbitrarily lower the seed and flood thresholds further without considering preventative ways to mitigate false detections will decrease the reliability.
Other source detection algorithms such as SExtractor (Bertin and Arnouts 1996) have a minimum island size (chosen by the user) to mitigate false detections. This is a useful variable to employ, but it is not automated. However, due to the nature of radio in- terferometric images during deconvolution, the image is convolved with a Gaussian PSF representation of the synthesised beam. Therefore, an infinitesimally small point source will assume the shape and size of the CLEANed beam, which in turn is comprised of a finite number of pixels. No real detectable source can be smaller than the beam with the addition of noise and therefore no island can have fewer numbers of pixels than the number of pixels comprising the beam with noise. Exploiting this condition inherent to radio interferometric images to the source detection mitigates the possibility of false detec- tions, even in very noisy images, or sources < 5σ. AEGEAN does consider the synthesised beam when fitting Gaussian components to the summits in the curvature maps for flux extraction, but not during the island flooding, i.e. source detection stage.
For the COBRaS source detection pipeline, it is necessary to push the limit of source detection below 5σ in order to allow the detection of as many OB stellar winds as possible and to achieve a high completeness of OB stars in Cyg OB2. This condition is an important modification to the current floodfill algorithm utilised by AEGEAN and BLOBCAT.
The other modification to the COBRaS source detection is another additional run of the floodfill algorithm with a lower flood threshold to enable more source pixels to be added to the island. This is because the COBRaS algorithm will either fit Gaussians via theAIPStaskJMFIT(least squares method to fit a Gaussian given a small search region) or calculate the flux directly from the island pixels (see Section 4.3.1). If the fluxes are derived from JMFIT, then only one run of the floodfill algorithm is required to obtain the max pixel position and pixel flux inputs for JMFIT.
This method has the benefits of the restrictive conditions above by having a stricter flood threshold when identifying sources, and then relaxing this flood threshold when the source islands are known. It is necessary to flood the island as much as possible for accurate flux determination from individual island pixels.
Another important aspect to the COBRaS source detection pipeline is that all the pixels within an island are assumed to be from a single source. This is because the flux determination is conducted on a pixel-by-pixel basis, summed and corrected for the
background noise to produce a flux independent from Gaussian fits. Certain expected sources in the COBRaS field are intrinsically non-Gaussian in shape. For example, binary interaction regions assume a crescent shape as the more powerful wind of one of the components of the binary distorts the adiabatic shock region. The absolute shape of the region depends on the orbital positions of the binary components. To achieve an accurate flux measurement, non-Gaussian determinations are required. Previously, the fluxes from these regions were determined by Gaussian fits (typicallyJMFIT, e.g. Watson et al. 2002) with manual TVSTAT checks (Dougherty et al. 2005), and as stated above is an option within the pipeline.