radiosed – I. Bayesian inference of radio SEDs from inhomogeneous surveys

• Published in: Monthly notices of the Royal Astronomical Society Vol. 533; no. 4; pp. 4248 - 4267
• Main Authors: Kerrison, Emily F, Allison, James R, Moss, Vanessa A, Sadler, Elaine M, Rees, Glen A
• Format: Journal Article
• Language: English
• Published: London Oxford University Press 01.10.2024
• Subjects: Active galactic nuclei; Bayesian analysis; Celestial bodies; Image resolution; Modularity; Radio astronomy; Radio emission; Radio jets (astronomy); Radio sources (astronomy); Radio telescopes; Sky surveys (astronomy); Statistical inference; software: data analysis; software: public release; galaxies: nuclei; radio continuum: galaxies
• ISSN: 0035-8711; 1365-2966
• DOI: 10.1093/mnras/stae1796

ABSTRACT We present here radiosed, a Bayesian inference framework tailored to modelling and classifying broad-band radio spectral energy distributions (SEDs) using only data from publicly released, large-area surveys. We outline the functionality of radiosed, with its focus on broad-band radio emissions that can trace kiloparsec-scale absorption within both the radio jets and the circumgalactic medium of active galactic nuclei (AGN). In particular, we discuss the capability of radiosed to advance our understanding of AGN physics and composition within youngest and most compact sources, for which high-resolution imaging is often unavailable. These young radio AGN typically manifest as peaked spectrum sources that, before radiosed, were difficult to identify owing to the large, broad-band frequency coverage typically required, and yet they provide an invaluable environment for understanding AGN evolution and feedback. We discuss the implementation details of radiosed, and we validate our approach against both synthetic and observational data. Since the surveys used are drawn from multiple epochs of observation, we also consider the output from radiosed in the context of AGN variability. Finally, we show that radiosed recovers the expected SED shapes for a selection of well-characterized radio sources from the literature, and we discuss avenues for further study of these and other sources using radio SED fitting as a starting point. The scalability and modularity of this framework make it an exciting tool for multiwavelength astronomers as next-generation telescopes begin several all-sky surveys. Accordingly, we make the code for radiosed, which is written in python, available on GitHub.