Towards the cross-identification of radio galaxies with machine learning and the effect of radio-loud AGN on galaxy evolution
Creators: name: Alegre, Lara
Publisher: name: The University of Edinburgh
Year: 2023
Subjects: radio galaxies
cross-identi cation of radio galaxies
machine learning
radio-loud AGN
galaxy evolution

description: It is now well established that active galactic nuclei (AGN) play a fundamental role in galaxy evolution. On cosmic scales, the evolution over cosmic time of the star-formation rate density and black hole accretion rate appear to be closely related, and on galactic scales, the mass of the stellar bulge is tightly correlated to the mass of the black hole. In particular, radio-loud AGN, which are characterised by powerful jets extending hundreds of kiloparsecs from the galaxy, make a significant contribution to the evolution of the most massive galaxies. There exists a correlation between the prevalence of radio-loud AGN and the stellar and black hole masses, with the stellar mass being the stronger driver of AGN activity. Furthermore, essentially all of the most massive galaxies host a radio-loud AGN. AGN feedback is the strongest candidate for driving the quenching of star-formation activity, in particular at galaxies at the highest masses, as it is capable of maintaining these galaxies as "red and dead". However, the precise mechanisms by which AGN influence galaxy evolution remain poorly understood. The anticipation of the Square Kilometre Array (SKA) brought radio astronomy into a revolutionary new era. New-generation radio telescopes have been built to develop and test new technologies while addressing different scientific questions. These have already detected a large number of sources and many previously unknown galaxies. One of these telescopes is the Low Frequency Array (LOFAR), which has been conducting an extensive survey across the entire northern sky called the LOFAR Two-Metre Sky Survey (LoTSS). In LoTSS, the source density is higher than in any existing large-area radio survey, and in less than a third of the survey, LoTSS already detected more than 4 million radio sources. The large size of the LoTSS samples already allows the separation of the AGNs into bins of stellar mass, environment, black hole mass, star formation rate, and morphology independently, thus enabling the breaking of degeneracies between the different parameters. The radio, long used to identify and study AGNs, is a powerful tool when radio sources are matched to their optically identified host galaxies. This "cross-matching" process typically depends on a combination of statistical approaches and visual inspection. For compact sources, cross-matching is traditionally achieved using statistical methods. The task becoms significantly more difficult when the radio emission is extended, split into multiple radio components, or when the host galaxy is not detected in the optical. In these cases, sources need to be inspected, radio components need to be eventually associated together into physical sources, and then radio sources need to be cross-matched with their optical and/or infrared counterparts. With recent radio continuum surveys growing massively in size, it is now extremely laborious to visually cross-match more than a small fraction of the total sources. The new high-sensitivity radio telescopes are also better at detecting complex radio structures, resulting in an increase in the number of radio sources whose radio emission is separated into different radio components. In addition, due to a higher density of objects, more compact sources can be randomly positioned close enough to resemble extended sources. Consequently, the cross-matching of radio galaxies with their optical counterparts is becoming increasingly difficult. It is crucial to minimise the extent of unnecessary inspection, with the present cross-matching systems demanding improvement. In this thesis, I use Machine Learning (ML) to investigate solutions to improve the cross-matching process. ML is a rapidly evolving technique that has recently benefited from a vast increase in data availability, increased computing power, and significantly improved algorithms. ML is gaining popularity in the field of astronomy, and it is undoubtedly the most promising technique for managing the large radio astronomy datasets, while having available at the same time the amount of data required to train ML algorithms. Part of the work in this thesis was indeed focused on creating a dataset based on visual inspections of the first data release of the LoTSS survey (LoTSS DR1) in order to train and cross-validate the ML models, and apply the results to the second data release (LoTSS DR2). I trained tree-based ML models using this dataset to determine whether a statistical match is reliable. In particular, I implemented a classifier to identify the sources for which a statistical match to optical and infrared catalogues by likelihood ratio is not reliable in order to select radio sources for visual inspection. I used the properties of the radio sources, the Gaussians that compose a source, the neighbouring radio sources, as well as the optical counterparts. The best model, a gradient boosting classifier, achieves an accuracy of 95% on a balanced dataset and 96% on real unbalanced data after optimising the classification threshold. The results were incorporated in the cross-matching of LoTSS DR2. I further present a deep learning classifier for identifying sources that require radio component association. In order to improve spatial and local information about the radio sources, I create a multi-modal model that makes use of different types of input data, with a convolutional network component of the model receiving radio images as input and a neural network component using parameters measured from the radio source and its near neighbours. The model helps to recover 94% of the sources with multiple components in balanced dataset and has an accuracy of 97% on real unbalanced data. The method has already been applied with success to properly identify sources that require component association in order to get the correct radio fluxes for AGN population studies. The ML techniques used in this work can be adapted to other radio surveys. Furthermore, ML will be crucial to dealing with the next radio surveys, in particular for source detection, identification and cross-matching, where only with reliable source identification is it possible to combine radio data with other data at different wavelengths and maximally exploit the scientific potential of the radio data. The use of deep learning, in particular testing ways of combining different data types, can bring further advantages, as it may help with the comprehension of data with different origins. This is particularly important for any upcoming data integration within the SKA. Finally, I used the results of cross-matching the LoTSS DR2 data to understand the interaction between radio-loud AGN, the host galaxy, and the surrounding environment. Specifically, the investigation focused on the properties of the hosts of radio-loud AGN, such as stellar mass, bulge mass, and black hole mass, as well as morphology and environmental factors. The results consistently support the significant influence of stellar mass on radio-AGN activity. It was found that galaxy morphology (i.e. ellipticals vs. spirals) has a negligible dependence on AGN activity unless at higher masses, but those correlate with stellar mass as well as with the environment. The most relevant factor for radio AGN prevalence, after controlling for stellar mass, emerged as higher-density environments, in particular on a global scale. These outcomes provide valuable insights into the triggering and fuelling mechanisms of radio-loud AGN, aligning with cooling flow models and improving our understanding of the phenomenon.