Description
Black hole jets have shaped our Universe. These relativistic jets (known as Active Galactic Nuclei [AGN]) heat and transport gas in their host galaxy and are a fundamental component of galaxy evolution. Despite their importance, understanding the energetics and environments of these extreme objects remains difficult. This is due, in part, to the requirement of overlapping, high resolution observations at radio and X-ray frequencies. Currently we only have adequate data for a few objects, despite a wealth of radio observations from all-sky surveys. This problem will compound on the release of the new generation radio telescope, the SKA Observatory, which is expected to observe thousands of AGNs.
I present a user-friendly package that derives the energetics and environments of observed AGN. I use the Radio AGN in Semi-analytic Environments (RAiSE) code as a forward model which produces synthetic images of AGNs like they appear on the sky. I use a novel Bayesian approach to match RAiSE with the observations which takes advantage of RAiSE's ability to generate synthetic images at different resolutions. I implement a two-layer Markov Chain Monte Carlo (MCMC) model which drastically improves the computational efficiency of the parameter inversion. This results in energetics and environment estimate in under ten minutes, where similar packages requires two days.
Using only radio observations, this package can derive the energetics and environments that match other derivations of observed AGN. It probes the density of the gas into which the AGN is expanding (ie their environments) despite such gas being invisible at radio wavelengths. I use this package to study Giant Radio Galaxies; AGN jets that extend beyond 3 Mpc (~9.8 million light years) and show that these galaxies are an older sub-population of AGN.
| I am the presenting author | Yes |
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