Search and destroy SAD

In order to both validate and compare the results obtained using SEAC, the inbuilt AIPS task Search and Destroy (SAD) can also be implemented upon the COBRaS L-band data. SAD attempts to find sources whose peak pixel flux is brighter than a given level. Those identified that are contiguous become merged into one island from which JMFIT is used to perform a least square Gaussian fit to the identified pixels. The derived parameters including the position and integrated flux of each island are calculated directly from the Gaussian model which are then written into a Model Fit (MF) table, attached to the image. JMFIT can fit up to a maximum of four Gaussian components to each island but returns reliable positions and flux densities for point-like, Gaussian sources with a good signal-to-noise ratio.

For a direct comparison between the two source extraction methods, each algorithm was implemented on four sources found within the COBRaS L-band field of view. The sources were chosen in order to give a fair representation of the different types of radio sources within the COBRaS data set. This included a variation in the signal-to-noise ratio, peak- pixel flux, and the size of each source on the sky with respect to the beam size. Figure

3.3 shows two plots of each of the four chosen sources. Firstly a plot created using the AIPS task KNTR, from which the beam size (found in the bottom left hand corner of each image) can be directly compared to the extent of the source on the sky. Secondly, on the right hand column of Figure3.3, the output plots from SEAC are shown, including

Figure 3.2: A typical example of the output plots from SEAC. The pro- gram was run with a seed and flood threshold of 5σ and 4σ respectively on a 2048 × 2048 image of a region of the sky within the field of view of pointing E from the COBRaS L-band observations. The two sources within the image which were detected are shown in the bottom four panels. The top left panel shows a plot of the input image with the two detected sources highlighted appropriately. Additionally, the top right image shows

3.1. Source detection and flux extraction 163

1.

2.

3.

4.

Figure 3.3: Four different sources within the COBRaS L-band field of view, the left hand column shows the plots made with the AIPS task KNTR whilst the right side shows the output png files from SEAC. 1. Cyg OB2 #12; 2. A11 (MT267); 3. SBHW90; 4. Cyg OB2 #9. The positions and integrated fluxes for each of the four sources, as determined by SEAC and

Table 3.1: To highlight the differences between the positions and inte- grated fluxes as obtained via SEAC and SAD for four different sources of varying brightness and size. Both algorithms are detecting sources down to 5 × the image RMS (5σ; SEAC is using an additional flood threshold σF=4σ). The corresponding images of these four sources can be seen in

Figure3.3.

Source Name 1. A11 2. CYG#12 3. CYG#9 4. SBHW90

RA (J2000)* 20 32 31.527 20 32 40.958 20 33 10.729 20 32 56.792 DEC (J2000)* 41 14 08.128 41 14 29.220 41 15 08.145 41 08 53.364

Resolved? No Yes No Yes

S/N* 9 36 51 312

Position Offset 37 mas 6 mas 11 mas 143 mas

FSEAC(µJ y) 185 ± 22 921 ± 50 1377 ± 48 13826 ± 225

FSAD(µJ y) 280 ± 39 1131 ± 72 1424 ± 142 8659 ± 108

*As measured using the results from SEAC.

the detected area of each island obtained using the floodfill algorithm. Having run both SEAC and SAD upon each of the four images, Table3.1shows the difference between their respective results including the offset in the derived source position from each algorithm and the integrated flux density of the source found by either method.

The image of source number 3, the colliding wind massive star binary known as Cyg OB2 #9 (see Chapter5for more details), is compact, includes a good signal-to-noise ratio and is only marginally resolved within the e-MERLIN L-band observations. As a result, this source can be well represented by a Gaussian model leading to a reliable flux density and position as determined by SAD. With reference to Table3.1the derived flux densities from both SAD and SEAC are within ∼ 3 % of one another showing the reliability of either source extraction method for this type of source. The similarity between the derived flux densities between the two extraction algorithms starts to break down when different types of sources are encountered. The blue hypergiant (and candidate Luminous Blue Variable) star Cyg OB2 #12 (see Chapter 4 for further details) shown as source number 2 in both Table3.1and Figure3.3still has a decent signal-to-noise ratio yet is mostly resolved within the COBRaS L-band observations. Whilst it can be still represented by a Gaussian model, the source structure leads to a slightly larger (∼ 20%) difference between the derived flux densities of SEAC and SAD.

3.1. Source detection and flux extraction 165

A11 (source number 1), a binary system with an O7.5 iii primary, represents a very com- pact, un-resolved source of a low signal-to-noise ratio. The difference in the derived flux density from both SEAC and SAD is a factor of ∼ 1.5, a significant difference for a source with a low signal-to-noise. If the floodfill threshold, σf, is dropped to 3.0 × the image

RMS, SEAC provides an integrated flux density of 219 ± 26 µJy for A11. This shows that for islands with a low signal-to-noise ratio, the full extent of the source can be lost within the surrounding noise level and SEAC can potentially underestimate their flux densities. In contrast, SAD is unlikely to underestimate the flux density of low signal-to-noise source, however with such little information regarding the true extent of the source on the sky, the Gaussian fit is unlikely to well represent the source and may potentially lead to un-reliable flux density measurements.

At the other end in the spectrum of source types, SBHW90, an unclassified object rep- resents a resolved, bright radio source with lots of extended structure. The flux density derived from SEAC is ∼ 1.6 × larger than that derived from SAD. This highlights the difficulty in accurately representing resolved, extended sources with up to four Gaussian components, as in the case with SAD. This small study into the difference between the two source detection algorithms shows that SEAC will be better implemented upon the COBRaS 21cm radio maps. SEAC has been shown to be versatile, delivering reliable source positions and flux densities for a range of source types that differ in their structure, signal-to-noise, and extent on the sky.

Further comparisons between the source detection efficiency and flux determinations ob- tained between JMFIT (i.e. SAD) and pixel-by-pixel (PP; i.e. SEAC) methods were made in Peck (2014). By testing these methods on simulated point-like and resolved Gaussian sources at a range of signal-to-noise ratios, Peck (2014) found that the PP method was shown to perform better in terms of flux extraction than JMFIT for sources with a SNR ≥ 5 (despite the non-Gaussian nature of the PP method). Moreover, for sources of a low SNR, the PP method was found to suffer in measuring their flux densities due to the in- trinsic property of source declaration as a function of the surrounding noise. JMFIT on the other hand was found to consistently underestimate the flux by ∼ 4% at higher SNRs whilst also producing a number of false detections for SNR ≤ 5.