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- Morabito, Leah K., et al.
(author)
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Identifying active galactic nuclei via brightness temperature with sub-arcsecond international LOFAR telescope observations
- 2022
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In: Monthly Notices of the Royal Astronomical Society. - : Oxford University Press (OUP). - 0035-8711 .- 1365-2966. ; 515:4, s. 5758-5774
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Journal article (peer-reviewed)abstract
- Identifying active galactic nuclei (AGNs) and isolating their contribution to a galaxy's energy budget is crucial for studying the co-evolution of AGNs and their host galaxies. Brightness temperature (T-b) measurements from high-resolution radio observations at GHz frequencies are widely used to identify AGNs. Here, we investigate using new sub-arcsecond imaging at 144 MHz with the International LOFAR Telescope to identify AGNs using T-b in the Lockman Hole field. We use ancillary data to validate the 940 AGN identifications, finding 83 percent of sources have AGN classifications from SED fitting and/or photometric identifications, yielding 160 new AGN identifications. Considering the multiwavelength classifications, brightness temperature criteria select over half of radio-excess sources, 32 percent of sources classified as radio-quiet AGNs, and 20 percent of sources classified as star-forming galaxies. Infrared colour-colour plots and comparison with what we would expect to detect based on peak brightness in 6 arcsec LOFAR maps imply that the star-forming galaxies and sources at low flux densities have a mixture of star-formation and AGN activity. We separate the radio emission from star-formation and AGN in unresolved, T-b-identified AGNs with no significant radio excess and find the AGN comprises 0.49 +/- 0.16 of the radio luminosity. Overall, the non-radio excess AGNs show evidence for having a variety of different radio emission mechanisms, which can provide different pathways for AGNs and galaxy co-evolution. This validation of AGN identification using brightness temperature at low frequencies opens the possibility for securely selecting AGN samples where ancillary data are inadequate.
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