RadioSED I: Bayesian inference of radio SEDs from inhomogeneous surveys

TL;DR

RadioSED is a Bayesian framework designed to model and classify broadband radio spectral energy distributions from large-area surveys, aiding in understanding AGN physics, especially in young, compact sources.

Contribution

It introduces RadioSED, a novel, scalable Bayesian inference tool for radio SED analysis using publicly available survey data, with validation on synthetic and real sources.

Findings

Successfully recovers expected SED shapes.

Validates approach with synthetic and observational data.

Applicable to multi-epoch survey data.

Abstract

We present here RadioSED, a Bayesian inference framework tailored to modelling and classifying broadband radio spectral energy distributions (SEDs) using only data from publicly-released, large-area surveys. We outline the functionality of RadioSED, with its focus on broadband radio emissions which 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 (PS) sources which, before RadioSED, were difficult to identify owing to the large, broadband 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-characterised 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.