Welcome! I am a radio astronomer at Durham University. In my research, I focus on the effect that supermassive black holes have on the formation and evolution of galaxy clusters. In particular, I primarily study the radio properties of these black hole systems, which reveal how these black holes heat up the cluster environment and affect the formation of new stars.

Observationally, I specialize in high-resolution radio observations taken at low frequencies, and am therefore a member of the LOFAR Surveys KSP Collaboration and the Square Kilometer Array VLBI Science Working Group. Within these collaborations, I contribute to the development of calibration strategies and software, which have resulted in multiple observational breakthroughs.

Prior to my current position at Durham University, I obtained my PhD at Leiden Observatory. Before that, I studied at the University of Groningen.

Please feel free to contact me via email at roland.timmerman durham.ac.uk

New high-resolution radio images of galaxy clusters made with LOFAR

Following the release of the new LOFAR-VLBI pipeline, we published a series of high-resolution radio maps of active galactic nuclei in galaxy clusters. Notably, these maps beautifully show the interaction between the magnetized plasma orginating from the central AGN (shown here in red) with the surrounding intracluster medium (shown in blue). Thanks to these new observations, we are able to measure the total energy output of these AGNs in more detail, helping us understand how galaxy clusters form. Currently, we are working on using these new observational capabilities to study these systems in the Early Universe.

Our paper on these results was published in the peer-reviewed journal Astronomy & Astrophysics.

Deep radio observations of galaxy clusters in the Early Universe

Following up on our previous project in the nearby Universe, we received 50 hours of LOFAR observing time on a sample of high-redshift galaxy clusters. Using the resulting images, we are able to study the effect supermassive black holes have on the formation and evolution of galaxy clusters. The example shown here, with the 144 MHz radio emission shown in red, shows part of a galaxy cluster at less than half the current age of the Universe. Two radio lobes formed by jetted outflows from the central supermassive black hole are shown to shoot out of their galaxy and into the cluster environment. These are some of the most distant radio lobes ever observed with such rich detail.

Our paper on these results was published in the peer-reviewed journal Astronomy & Astrophysics.

Porphyrion: the discovery of a record-breaking Giant Radio Galaxy

Our team, led by Martijn Oei, discovered the radio galaxy Porphyrion in the LOFAR Two-Metre Sky Survey. This radio galaxy, spanning 7 megaparsecs or approximately 140 times the diameter of our Milky Way galaxy, has broken the previous record of Alcyoneus (also a discovery of Oei et al.). Notably, Porphyrion is the first known radio galaxy to reach scales comparable to the size of voids of the Cosmic Web, implying that there is not a single location in the Universe isolated enough to be free from the impact of black hole feedback. With deeper surveys and next-generation instruments, we will try to learn if Porphyrion is unique, or if it just the tip of the iceberg...

Our paper on these results was published in Nature.

Radio image redshifter

Understanding the observational biases that affect measurements at different redshifts can be challenging. However, by being able to virtually place a radio source at a different distance, it is possible to gain some critical first-order insight. To do this as accurately as possible, I have written a Python code that rescales the radio map using the CLEAN model components based on the angular diameter and luminosity distances, and the integrated spectral index. Finally, the rescaled CLEAN model is convolved with the beam and either the original CLEAN residual map or a random Gaussian noise map is added. The resulting image then provides the best estimate of what the source would look like if it were at a different redshift.

All relevant Python code is available on my GitHub.

Asymmetric uncertainty Monte Carlo

Determining accurate uncertainties is a key part of any scientific measurement. Monte Carlo approaches provide a robust strategy to estimate the uncertainty on the output of arbitrarily complex mathematical operations. A subtle but fundamental step of such an approach is generating random samples according to a representative distribution, such as a normal distribution. However, especially when different quantities relate to each other in non-linear ways, uncertainties can become asymmetric. This makes it difficult to use values reported in literature while maintaining the correct uncertainties. To resolve this, I have written a Python code to generate random samples according to a skewed normal distribution, defined simply by the best value and the uncertainties on that value.

All relevant Python code is available on my GitHub.