Bonny Barkus
Cross-identification of radio sources with optical and infrared catalogues is essential for determining host properties and distances, leading to intrinsic properties such as luminosity and size; but it is also far from straight forward. For simple, compact or isolated sources this can be done in an automated fashion. However, for extended sources or those which contain multiple components this becomes more complicated and has more often been achieved through human classification. As surveys become larger and sources more numerous this method becomes less efficient. The LOFAR Two metre Sky Survey (LoTSS) is the largest radio survey to date in terms of numbers of sources and data volume and is sensitive to both compact and extended emission, making it ideal for the study of radio sources. Using the first data release from LoTSS, and studying the morphology of the largest, brightest, more complex structures; the innovative idea of ridgelines, tracing the path of a jet, to link radio sources to their host galaxies has been applied. My poster demonstrates the ongoing work and potential of this method for improving automated identification of the host galaxies of radio sources.
Different radio continuum surveys observe over different frequency ranges and sky areas providing large samples of radio galaxies to study. These are structures created by relativistic jets and are typically far larger than the galaxies which host them. These galaxies are invisible in radio but can be detected in optical/infrared. For many science goals host and redshift identifications are needed and can be obtained through cross-identification with optical/infrared surveys (Duncan, 2019). This is essential and difficult; with simple, compact objects it can be automated, otherwise human classification is required on the more complex sources (Duncan, 2019, Williams, 2019).
The aim of this poster is to introduce ridgelines and how they can be used to improve the likelihood ratio used for LoTSS DR1; hence reducing the need for human classifiers such as LOFAR Galaxy Zoo.
The LOFAR Two-metre Sky Survey (LoTSS) is the main continuum survey for LOFAR (LOw Frequency ARray) covering a frequency range of 120 – 168 MHz with a resolution of 6 arcsecs. Completed it will cover 424 deg2 of the northern hemisphere. Data Release 1 contains ~320,000 sources of which ~23,000 were classed as active galactic nuclei (AGN) by Hardcastle et al. (2019).
In LoTSS DR1 two methods were implemented for cross-identification of radio sources with optical/infrared counterparts. For sources < 30 arcsecs Williams, et al. (2019) applied a likelihood ratio using colour, magnitude and distance from LOFAR catalogue position for a statistical determination. If the source was large or complex it was sent to LOFAR Galaxy Zoo (LGZ) for a team of citizen scientists to determine. In DR1 ~12,000 sources went to LGZ for classification. A sample of 991 sources > 60 arcsecs and with a flux > 30 mJy was selected from the AGN group.
Following a similar approach to Pushkarev et al. (2017)’s total intensity ridgelines. A ridgeline is defined as the pathway of connected points, of highest intensity, tracing the direction of the jet. To calculate the ridgeline, the initial point is selected at the point of maximum flux on the source. Two search directions and cones are determined and the first point in each direction is calculated. Ridge points are defined as being the point of highest flux a beams width away from the previous point inside the search cone. The new point is used as the start for the next search, and the previous points are masked around with a circle one beams width in radius to prevent any points from being found in a previous search area. Once the edge of the source is reached, the points are connected to form the ridgeline. In the sample of 991, 96% of the sources successfully drew ridgelines.
Calculating the likelihood ratio using the shortest distance to the ridgeline, the distance to the LOFAR catalogue and applying Williams, et al. (2019) magnitude method; then comparing the maximum value with known hosts gives a success rate of 64% using the likelihood ratios from the ridgeline and LOFAR multiplied together and 69% with the inclusion of magnitude (for w1-band).
The average brightness along the ridgeline can be determined and is the surface brightness profile, classifying distances along a ridgeline. The next steps to improving the likelihood ratio are including colour, and distance along using surface brightness profiles.
Duncan K. J., et al., 2019, A&A, 622
Hardcastle M. J., et al., 2019, A&A, 622, 12
Pushkarev A. B., et al., 2017, MNRAS, 468, 4992
Williams W. L., et al., 2019, A&A, 622