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... Show moreWe 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. Show less
Mostert, R.I.J.; Morganti, R.; Brienza, M.; Duncan, K.J.; Oei, M.S.S.L.; Röttgering, H.J.A.; ... ; Jurlin, N. 2023
Super massive black holes at the centres of galaxies can cycle through periods of activity and quiescence. Characterising the duty cycle of active galactic nuclei (AGN) is crucial for understanding... Show moreSuper massive black holes at the centres of galaxies can cycle through periods of activity and quiescence. Characterising the duty cycle of active galactic nuclei (AGN) is crucial for understanding the impact of the energy they release on the host galaxy. For radio AGN, this can be done by identifying dying (remnant) and restarted radio galaxies from their radio spectral properties. Using the combination of the images at 1400 MHz produced by Apertif, the new phased-array feed receiver installed on the Westerbork Synthesis Radio Telescope, and images at 150 MHz provided by LOFAR, we have derived resolved spectral index images (at a resolution of similar to 15 arcsec) for all the sources within an approximately 6 deg(2) area of the Lockman Hole region. In this way, we were able to select 15 extended radio sources with emission (partly or entirely) characterised by extremely steep spectral indices (steeper than 1.2). These objects represent cases of radio sources in the remnant or the restarted phases of their life cycle. Our findings confirm that these objects are not as rare as previously thought, suggesting a relatively fast cycle. They also show a variety of properties that can be relevant for modelling the evolution of radio galaxies. For example, the restarted activity can occur while the remnant structure from a previous phase of activity is still visible. This provides constraints on the duration of the "off" (dying) phase. In extended remnants with ultra-steep spectra at low frequencies, the activity likely stopped a few hundred megayears ago, and they correspond to the older tail of the age distribution of radio galaxies, in agreement with the results of simulations of radio source evolution. We find remnant radio sources with a variety of structures (from double-lobed to amorphous), possibly suggesting different types of progenitors. The present work sets the stage for exploiting the powerful tool of low-frequency spectral index studies of extended sources by taking advantage of the large areas common to the LOFAR and the Apertif surveys. Show less
