GRG constraints from ML and Bayesian inf.

We combine five existing codes into a single machine learning-driven (ML) pipeline that automates radio source component association and optical host identification for well-resolved radio sources. We create a radio--optical catalogue for the entire LoTSS Data Release 2 (DR2) footprint and subsequently select all sources that qualify as possible giants. We combine the list of ML pipeline GRG candidates with an existing list of LoTSS DR2 crowd-sourced GRG candidates and visually confirm or reject all members of the merged sample.

To infer intrinsic GRG properties from GRG observations, we develop further a population-based forward model and constrain its parameters using Bayesian inference. Roughly half of all radio sources that our ML pipeline identifies as giants (of at least l_(p,GRG)_ 0.7Mpc long) indeed turn out to be upon visual inspection, whereas the success rate is one in eleven for the previous best giant-finding ML technique in the literature.

We confirm 5647 previously unknown giants from the crowd-sourced LoTSS DR2 catalogue and 2597 previously unknown giants from the ML pipeline. Our confirmations and discoveries bring the total number of known giants to at least 11,585. Our forward model for the intrinsic GRG population is able to provide a good fit to the data.

Our posterior indicates that the projected lengths of giants are consistent with a curved power law probability density function whose initial tail index xi(l_(p,GRG)_)=-2.8+/-0.2 changes by {Delta}xi=-2.4+/-0.3 over the interval up to l_p_=5Mpc. We predict a comoving GRG number density n_GRG_=13+/-10(100Mpc)^-3^, close to a current estimate of the number density of luminous non-giant radio galaxies. With the projected length distribution, number density, and additional assumptions, we derive a current-day GRG lobe VFF V_(GRG-CW)_(z=0)=1.1+/-0.9x10^-5^ in clusters and filaments of the Cosmic Web.

We have created a state-of-the-art ML-accelerated pipeline for finding giants, whose complex morphologies, arcminute extents, and radio-emitting surroundings pose challenges. Our data analysis suggests that giants are more common than previously thought. More work is needed to make estimates of the GRG lobe VFF reliable, but the first results indicate that it is possible that magnetic fields originating from giants permeate significant (~10%) fractions of today's Cosmic Web.