Speaker
Description
The faint radio-source population includes sources dominated both by star formation and active galactic nuclei (AGN), encoding the evolution of activity in the Universe. Investigating its nature requires accurately identifying the dominant source of emission in each system - a task that spectroscopy is uniquely well-suited to address. To demonstrate this, we make use of available spectra provided by the Dark Energy Spectroscopic Instrument (DESI) to probabilistically classify 5,173 z~1 radio sources from the LoTSS Deep Fields.This was done by combining three tools: (i) the identification of a radio excess, (ii) the BPT diagram, and (iii) a modified Mass Excitation diagram, alongside Monte Carlo methods to estimate the probability that each source is either a star-forming galaxy (SFG), a radio-quiet AGN (RQ AGN), or a high-\slash low-excitation radio galaxy (HERG or LERG). This approach extends the probabilistic classification framework of previous works by nearly doubling the redshift range, such that we can now probabilistically classify sources over the latter half of cosmic history. Using a 90 per cent reliability threshold, we find reasonable overall agreement (~77 per cent) with state-of-the-art photometric classifications, but significant differences remain, including that we identify 2-5 times more RQ AGN. Furthermore, our high-confidence spectroscopic classifications show that radiatively-efficient and inefficient AGN exhibit clearly distinct Eddington-scaled accretion rate distributions, contrary to recent findings in the literature. Overall, our results highlight the need for new and forthcoming spectroscopic campaigns targeting radio sources, on the pathway to the SKA. In particular, with the launch of the WEAVE-LOFAR survey, we will obtain over a million spectra of sources identified in the LOFAR surveys. This dramatic increase in sample size will allow us to probe the interplay between star formation and AGN activity, as well as the different accretion modes, thereby providing us with a deeper and more complete understanding of the low-frequency radio population.
