Future work

The COBRaS observations taken on Apr. 11th and Apr. 26th of 2014 constitute the total awarded data allocation at L-band. An additional five hours of observations were

2.5. Conclusions and future work 147

Table 2.7: The final statistics of the fully calibrated COBRaS 21cm datasets. The integration times listed were taken from the final NX ta- ble within AIPS having re-run the AIPS task INDXR. The percentage of data remaining shows the amount of data left taking into account the num- ber of flags in time and frequency space still present within the data set. The resolutions (beam size) and sensitivities were obtained on a 512×512 image (cell size = 0.04") of the centre of each field, produced using IMAGR

with a natural weighting scheme.

Source Integration % Data Beam Sensitivity

Name Time (hrs) Remaining Size (mas) (µJy)

2014 Apr. 11th J2007+404 2.58 61.9% 205x167 435 CYG-L-A 0.73 55.3% 456x189 47 CYG-L-B 0.96 60.7% 192x161 42 CYG-L-C 0.96 61.0% 194x161 43 CYG-L-D 0.75 57.6% 434x226 41 CYG-L-E 1.10 58.0% 221x180 32 CYG-L-F 0.89 58.4% 258x207 37 CYG-L-G 0.72 56.2% 353x260 44 2014 Apr. 26th J2007+404 4.71 63.3% 196x178 202 CYG-L-A 3.05 50.9% 205x180 21 CYG-L-B 2.90 52.5% 205x172 21 CYG-L-C 2.97 53.2% 210x185 21 CYG-L-D 2.42 51.5% 205x157 24 CYG-L-E 2.90 53.0% 218x166 20 CYG-L-F 2.75 53.0% 199x163 22 CYG-L-G 2.79 48.6% 214x162 23

obtained on the 24th of January 2014 that have yet to be completely reduced due to their comparatively small amount of time spent on-source and a reduced data quality compared to their other epochs. However, its successful reduction will provide another observational epoch, at least for some of the brightest sources within the COBRaS L-band target fields. Furthermore, the u, v data from each of the three observation epochs will be co-added together from which single L-band maps can be forged of the entire dataset, increasing the overall sensitivity of the resulting images.

The predominant issue throughout the data reduction process has been the mitigation and treatment of Radio Frequency Interference (RFI). It is accountable for a large fraction of the data lost, meaning the final time spent on-source for each of the target fields across both of the observation epochs was only ∼ 1.5 hrs compared to the proposed 5 hrs, resulting in a lowering of the sensitivity of the radio maps by a factor of ∼√3.3. The only true solution would be to clear the frequency space from interference, yet in reality this is impossible without changing the location of the e-MERLIN array to somewhere very remote. To better mitigate the RFI, improved techniques must be implemented at both the pre- and post- correlation stages. SERPent for example will benefit from an option to alter the flagging parameters as a function of IFs, since IFs 1 and 2 were consistently shown to harbour significant amounts of RFI in comparison to others. If pre-correlation mitigation procedures could be implemented on the e-MERLIN array this could potentially hugely reduce the amount of RFI within the data, since corrupted signals from one antenna will not then be correlated with ‘clean’ signals from another.

A full study of all the sources of RFI across the L-band spectrum of the e-MERLIN array is necessary. This should be calculated as a function of all areas on sky across all hours of the day, throughout the course of an entire year. As such RFI could be more readily mitigated at the pre-correlation stage, providing a good model of the known RFI were readily available for a particular observation epoch. Furthermore, the known strong sources of RFI would then be less likely to ‘bleed’ or ‘spill’ into neighbouring channels leading to corruption on such large scales across the frequency space.

2.5. Conclusions and future work 149

array must become consistent in its data quality. With reference to Table2.7the sensitivity of the target field images of the Apr. 26th observations far outweigh those of the Apr. 11th, relative to the amount of time spent on-source. This is a result of inconsistent data quality, where the general noise within the Apr. 11th data is larger than that of the Apr. 26th. Moreover, all visibilities associated with the LL polarisation of Pickmere baselines were completely disregarded since their contribution significantly increased the noise within the resulting images. Such ‘mechanical’ issues must be rectified in order to deliver observations of sensitivity and resolution true to their potential.

151

Chapter 3

The COBRaS L-band all source

catalogue

This chapter presents the entire population of sources detected within the COBRaS L-band Legacy radio maps. Proceeding the complete reduction and imaging of the e-MERLIN L- band observations as described in Chapter2, this chapter begins by first depicting the tools used to provide reliable source detections across the seven target field pointings. In doing so, the Source Extraction Algorithm for COBRaS (SEAC) is presented and compared to existing source detection methods. Following a complete source extraction of the L- band maps including accurate positional information and integrated 21cm flux densities, the produced COBRaS L-band catalogue will be cross-correlated with existing catalogues of the Cygnus OB2 association. The cross-correlation process will separate out previously detected and identified sources whilst also providing a list of previously unidentified sources. In the characterisation of as many sources as possible, some fundamental analysis will be completed. The complete L-band source population catalogue presented here will form the basis of the entire COBRaS catalogue with this chapter acting as a guide for the future treatment of the COBRaS C-band (6cm) radio maps.

3.1

Source detection and flux extraction

In the advent of large observational surveys containing hundreds and thousands (and in the case of Gaia even billions) of sources, the need for efficient, robust and reliable source

detection algorithms has become ever more important. The challenge is to automate the detection process with the aim of minimising the number of false detections. Currently many different methods exist, most of which are tailored towards the specific characteristics of the image in question such as the types of sources present and the waveband at which the observations were taken (see Masias et al. 2012for a recent review).

In regards to the detection of sources within radio maps, recent work for the Evolutionary Map of the Universe (EMU) project that uses the Australian Square Kilometre Array Pathfinder (ASKAP) instrument produced two source detection codes. aegean (Hancock et al., 2012_{) and blobcat (Hales et al.,} 2012) both utilise the floodfill algorithm (see Section3.1.1.1_{) in order to first detect the sources within the field. aegean assumes each}

source is point like and can therefore be aptly represented by a Gaussian point spread function which is subsequently fitted to each detected source to obtain its flux. blobcat on the other hand performs both Guassian and non-Gaussain routines to fit each detected source and estimate its flux, allowing for a more accurate representation of resolved sources. The reliability and success rates of the floodfill algorithm implemented in either code made it a desirable choice for its application upon the COBRaS L-band radio maps.

Broadly speaking, source finding within radio astronomy involves finding a collection of pixels, each of which contain information regarding the radio flux of an astronomical source, and the shape of which contains morphological information of this source. The problem lies in being able to disentangle the surrounding background noise within the image from the pixels that actually contain source information. More specifically in regards to the observations made for the COBRaS project, many of the sources will be of a similar level to that of the surrounding noise meaning the boundary between real astronomical source and noise will often be blurred. Furthermore, the sizes, intensities and shapes of the astronomical sources present within the COBRaS field of view will undoubtedly vary dramatically, from resolved background radio galaxies to un-resolved, point-like stellar objects. This calls for a bespoke source extraction code that can return reliable source positions and fluxes for a wide range of different source types. Such a program is presented below in Section3.1.1.

3.1. Source detection and flux extraction 153