Machine learning for radio galaxy morphology analysis

We explored how to morphologically classify well-resolved jetted radio-loud active galactic nuclei (RLAGN) in the LOw Frequency Array (LOFAR) Two-metre Sky Survey (LoTSS) using machine learning.We investigated what morphology in total radio intensity maps can tell us about observed radio sources without complementary wavelength information and with limited visual inspection. We used a self-organising map (SOM) to model common radio morphologies and to reveal the rarest morphologies in LoTSS.Furthermore, we turned the radio source-component association problem into an object detection problem and trained an adapted Fast region convolutional neural network to mimic the grouping of source components into unique sources as performed by astronomers for LoTSS data.We also reduced the visual inspection required to find RLAGN remnant candidates based on their morphology, by using SOM-based features as input for a random forest classifier.Finally, we created a machine learning pipeline to identify giant radio galaxy (GRG) candidates and created a sample that contains more than ten thousand GRG. We then quantified the intrinsic GRG proper length distribution, the comoving GRG number density, and a current-day GRG lobe volume-filling fraction in clusters and filaments of the Cosmic Web.Large scale structure and cosmolog

Radio source-component association for the LOFAR Two-metre Sky Survey with region-based convolutional neural networks

Large scale structure and cosmolog

Finding AGN remnant candidates based on radio morphology with machine learning

Galaxie

The LOFAR Two-Metre Sky Survey: VI. Optical identifications for the second data release

The second data release of the LOFAR Two-Metre Sky Survey (LoTSS) covers 27% of the northern sky, with a total area of ~5700 deg1. The high angular resolution of LOFAR with Dutch baselines (6 arcsec) allows us to carry out optical identifications of a large fraction of the detected radio sources without further radio followup; however, the process is made more challenging by the many extended radio sources found in LOFAR images as a result of its excellent sensitivity to extended structure. In this paper we present source associations and identifications for sources in the second data release based on optical and near-infrared data, using a combination of a likelihood-ratio cross-match method developed for our first data release, our citizen science project Radio Galaxy Zoo: LOFAR, and new approaches to algorithmic optical identification, together with extensive visual inspection by astronomers. We also present spectroscopic or photometric redshifts for a large fraction of the optical identifications. In total 4 116 934 radio sources lie in the area with good optical data, of which 85% have an optical or infrared identification and 58% have a good redshift estimate. We demonstrate the quality of the dataset by comparing it with earlier optically identified radio surveys. This is by far the largest ever optically identified radio catalogue, and will permit robust statistical studies of star-forming and radio-loud active galaxies

Lockman Hole Apertif map at 1.4GHz

VizieR online Data Catalogue associated with article published in journal Astronomy & Astrophysics with title 'The best of both worlds: combining LOFAR and Apertif to derive resolved radio spectral index images.' (bibcode: 2021A&A...648A...9M

Lockman Hole Apertif map at 1.4GHz

VizieR online Data Catalogue associated with article published in journal Astronomy & Astrophysics with title 'The best of both worlds: combining LOFAR and Apertif to derive resolved radio spectral index images.' (bibcode: 2021A&A...648A...9M

The LOFAR Two-metre Sky Survey

In this data release from the ongoing LOw-Frequency ARray (LOFAR) Two-metre Sky Survey we present 120–168 MHz images covering 27% of the northern sky. Our coverage is split into two regions centred at approximately 12h45m +44◦300 and 1h00m +28◦000 and spanning 4178 and 1457 square degrees respectively. The images were derived from 3451 h (7.6 PB) of LOFAR High Band Antenna data which were corrected for the direction-independent instrumental properties as well as direction-dependent ionospheric distortions during extensive, but fully automated, data processing. A catalogue of 4 396 228 radio sources is derived from our total intensity (Stokes I) maps, where the majority of these have never been detected at radio wavelengths before. At 600 resolution, our full bandwidth Stokes I continuum maps with a central frequency of 144 MHz have: a median rms sensitivity of 83 µJy beam−1 ; a flux density scale accuracy of approximately 10%; an astrometric accuracy of 0.200; and we estimate the point-source completeness to be 90% at a peak brightness of 0.8 mJy beam−1 . By creating three 16 MHz bandwidth images across the band we are able to measure the in-band spectral index of many sources, albeit with an error on the derived spectral index of >±0.2 which is a consequence of our flux-density scale accuracy and small fractional bandwidth. Our circular polarisation (Stokes V) 2000 resolution 120–168 MHz continuum images have a median rms sensitivity of 95 µJy beam−1 , and we estimate a Stokes I to Stokes V leakage of 0.056%. Our linear polarisation (Stokes Q and Stokes U) image cubes consist of 480 × 97.6 kHz wide planes and have a median rms sensitivity per plane of 10.8 mJy beam−1 at 40 and 2.2 mJy beam−1 at 2000; we estimate the Stokes I to Stokes Q/U leakage to be approximately 0.2%. Here we characterise and publicly release our Stokes I, Q, U and V images in addition to the calibrated uv-data to facilitate the thorough scientific exploitation of this unique dataset