The astronomy group welcomes applications from postgraduates who wish to carry out research leading to the award of a Ph.D.
Applications are made using the online application form found here after registering a username and password. Further information on the admissions procedure is available from the postgraduate admissions tutor, Prof. Juan P. Garrahan. Once you have submitted your application, please also send a brief email containing your application ID to the astronomy admissions coordinator, Dr. Nina Hatch, confirming that you have applied.
Interviews for our STFC funded positions (for UK and international students who meet the appropriate eligibility requirements) are normally held during February each year. We therefore strongly encourage the submission of applications for this scheme before January 11th.
Overseas students may also be eligible for one of our international research scholarships, and should ensure their online application is submitted at least six weeks before the closing date for these schemes.
Examples of typical Ph.D. projects offered by the astronomy group may be found by following the research projects link on the right of this page. Further details regarding postgraduate funding opportunities are available at the links listed below, and more general information about being a postgraduate in the School of Physics and Astronomy may be obtained on our postgraduate study page.
The morphology, star-formation history, dynamics, and many other properties of galaxies depend strongly on where they formed and where they live. In other words, galaxies are affected by their environment. We have already studied in great detail the most extreme environments (cores of rich galaxy clusters), but milder environments such as the groups and the filamentary structures that feed galaxy clusters remain largely unexplored. We know that these environments must play an important role on galaxy evolution because many of the galaxies that fall into clusters have already been “pre-processed” by the time they arrive to the cluster core. In this project we will combine state-of-the-art numerical galaxy formation and evolution models with new and unique observational data taken with some of the most innovative astronomical instruments to, first, map these structures and identify the galaxies that inhabit them and, second, study their properties to find out how these environments affect their history.
Spiral arms are a familiar feature of galaxies, yet their origins are still poorly understood. A variety of mechanisms have been proposed, each predicting particular characteristics for the spiral patterns, as well as how they relate to a galaxy’s kinematics, star-formation, interstellar medium, and wider environment. Depending on which mechanism is at work, spiral arms may be superficial or significant, ephemeral or long-lived; meaningless or indicative. Small sets of nearby galaxies have provided support for many of the proposed mechanisms. However, in order to determine their relative importance across the galaxy population, it is essential to consider large, representative samples, spanning ranges of mass, environment and redshift. Measuring such detailed morphological features for large samples of galaxies is challenging. Recent advances have been made via two related paths: citizen science and deep (machine) learning. Thanks to the Galaxy Zoo project, we have visual information for hundreds of thousands of galaxies and the data to train the new generation of sophisticated computer vision models.
This project will verify and build upon a variety of recent work, utilising data from the latest generation of wide-and-deep surveys (e.g. DECaLS and KiDS). Visual information will be used to construct automatic classifiers that can not only reproduce humans, but surpass them, by avoiding their characteristic biases. Using these abilities, this project will examine correlations between observed spiral arm characteristics and other galaxy properties. By comparing these multivariate distributions with expectations from physical models we will determine the prevalence of each spiral arm mechanism across the galaxy population and through cosmic time; reaching a conclusion on the role of spiral arms in galaxy evolution.
One of the major unsolved problems in astrophysics is to understand why massive galaxies stopped forming stars. There are growing indications that feedback from supermassive black holes may play a crucial role, but so far the observational evidence is indirect and circumstantial. The aim of this project is to compare deep observational surveys of the distant Universe with the latest theoretical models, to determine which processes are likely to be dominant. Current models can include a wide range of quenching mechanisms (e.g. quasar heating, quasar winds, supernova-driven feedback, gas stripping), but these ideas are largely untested at high redshift. Theoretical predictions will be compared with a wide range of observed properties from the latest observational data (e.g. galaxy environments, sizes, morphologies, gas content, star-formation histories) to finally hone in on the key physical processes responsible for killing galaxies in the distant Universe.
The intergalactic medium forms the link between galaxy formation and cosmology; its spatial distribution is sometimes referred to as the cosmic web due to the filamentary network that intergalactic gas and dark matter traces on large scales. Detailed spectroscopic observations of the cosmic web -- as seen in absorption in the spectra of distant, background quasars -- therefore play a vital role in understanding the structural, chemical and thermal evolution of baryons across cosmic time. Unlocking and interpreting this rich source of information using high fidelity cosmological hydrodynamical simulations of intergalactic gas is the goal of this project. You will use the state-of-the-art Sherwood simulation suite to investigate the properties of intergalactic structure throughout cosmic time, and predict the observational signatures expected in the spectra of the most distant quasars and galaxies yet discovered.
Cosmological simulations play a critical role in predicting and interpreting the observed large scale distribution of baryons in the Universe -- the so called cosmic web. However, the speed, efficiency and accuracy of these simulations is sensitive to the numerical techniques and/or sub-grid physics implementations that are employed. This project will explore the predictions that widely used numerical hydrodynamics codes and cosmological intitial condition generators make for the distribution of intergalactic baryons at high redshift. The ultimate goal is to assess the systematic differences between these widely used codes, and establish an optimal route forward for the next generation cosmological simulations of the high redshift intergalactic medium. These models will play a central role in distinguishing between competing dark matter models (e.g. cold, warm or fuzzy dark matter) using forthcoming observations of the small scale distribution of the intergalactic gas in the early Universe. This project will involve using and modifying a variety of publicly available codes for masively parallel hydrodynamical simulations, and is best suited for a student with a strong interest in high performance computing and code development.
Our group plays a key role in the gravitational lensing element of the Herschel-ATLAS survey carried out by the Herschel Space Observatory. H-ATLAS is rapidly increasing the number of known strong lens systems with a continual stream of new detections at significantly higher redshifts than existing lens samples reach. This allows astronomers to probe deeper into the young Universe when galaxy evolution was in its early stages. These new lens systems are presently being followed up by the ground-breaking ALMA telescope in Chile which offers incredibly high resolution and sensitivity. This, in combination with the fact that the lenses themselves give a magnified image of the background source galaxy enables analysis of high redshift galaxies at a level of precision that is impossible to obtain otherwise. The project being offered is to carry out lens modelling of the ALMA observations to learn about both the foreground galaxy doing the lensing and the background galaxy being lensed. The project includes the possibility of developing lens modelling techniques.
Strong gravitational lensing is now recognised as one of the most powerful methods for measuring the amount and distribution of dark matter and baryonic mass in galaxies. Such measurements are key for the understanding of galaxy formation and evolution but there are presently only around 150 strong galaxy lens systems known and most of these are at low redshifts where the pace of galaxy evolution has significantly slowed. This situation is expected to improve dramatically in the near future with new higher redshift lens samples containing tens of thousands of lenses resulting from two forthcoming facilities: The Large Synoptic Survey Telescope which comes online in 2019 and the Euclid satellite due for launch in 2020. Our group has developed a technique that has now become the 'industry standard' for modelling strong lenses but the method is too labour intensive for scaling up to the large datasets anticipated. This project is therefore concerned with tackling this challenge directly by developing methods for the automatic detection and modelling of strong lens systems in large survey data.
Most galaxies in the Universe live in groups or clusters, making such large-scale structure critical both for studies of cosmology and of galaxy evolution. This project builds on a successful research program working at the interface between simulations (Pearce) and observations (Gray) to understand the physical processes that influence these objects and the galaxies inhabiting them. Students will exploit state-of-the-art N-body and hydrodynamic simulations, galaxy evolution models, and large imaging and spectrographic surveys to study the properties of large scale structure in both the real and mock universes. Comparison of both approaches allows us to simultaneously test the model physics, gain insight into the data, and understand the ultimate limitations of our measurements. Our goals include understanding group and cluster assembly (and implications for large cosmological surveys) as well as distentangling the interplay between galaxy properties and their environments.
The forthcoming UK-led WEAVE-LOFAR (W-L) survey will gather more than a million spectra of star-forming galaxies (SFGs) and active galactic nuclei (AGN) to reveal the most energetic phases of galaxy evolution. These energetic phases are instrumental in shaping the evolution of galaxies. For example, a galaxy undergoing a starburst can double its mass in only a few hundred Myr, whilst a powerful quasar releases enough energy to blow out all the cold gas from its host. Most massive galaxies have gone through these phases at least once in their lifetime (Hopkins et al. 2007, ApJS, 175, 356), but they pass so rapidly that active galaxies are rare and not found in large numbers in traditional spectroscopic surveys. W-L will solve this by observing the largest and most complete sample of active galaxies. We will lead the exploitation of this survey by focussing on the impact of supermassive black hole and stellar feedback on galaxy evolution.
Cosmological simulations predict pervasive cosmic gas accretion onto galaxies, but observations have shown that only a relatively small fraction of baryons end up as cold gas or stars within galaxies. To account for this discrepancy, theory invokes feedback from massive stars, supernovae and supermassive black holes in active galactic nuclei (AGN), to heat and remove the gas from galaxies. The most direct observational evidence of this feedback comes from measurements of gaseous outflows from SFGs and AGN, and whilst observations of outflows are ubiquitous (Veilleux et al. 2005, ARAA, 43,769) they have not yet provided unambiguous constraints to feedback models. Two key questions remain: (i) what is the efficiency of the feedback (measured as the ratio of star formation rate or AGN power to outflow rate), and (ii) what happens to the expelled gas (measured as the fraction of outflow that escapes the potential of the host galaxy)? W-L is able to answer these questions, as it is a radio-selected survey. Since W-L directly selects sources on star formation rate and AGN power without being inhibited by dust, it enables us to make the cleanest measurement of feedback efficiency. In this project you will lead the analysis of these spectra to measure the efficiency of AGN and stellar feedback and determine what happens to the expelled gas, thereby obtaining precise constraints of feedback that will be used in the next generation of galaxy formation models.
At the heart of most, if not all, massive galaxies lies a supermassive black hole (SMBH; Ferrarese & Ford, 2005, Space Sci. Rev.116, 523). SMBHs grow through the self-regulated accretion of material, during which time they present as active galactic nuclei (AGN) and release enormous amounts of radiative and mechanical energy. This energy impacts both their host galaxy, by driving gaseous outflows, and their larger environment through heating the surrounding intergalactic or intracluster medium. The ubiquity of SMBHs, however, contrasts with the paucity of AGN. Less than 10% of massive galaxies are AGN (Martini et al., 2013, ApJ, 768, 1), meaning that SMBHs lie in a dormant phase for most of their lifetime. This presents a conundrum as most massive galaxies have a ready supply of hot or cold gas which could feed the SMBH. Given the link between SMBH and galaxy growth (Ferrarese & Ford, 2005), as well as the devastating impact of AGN feedback on galaxy evolution (Bower et al. 2012, MNRAS, 422, 2816), our lack of understanding of what triggers AGN is a key unresolved issue in galactic astrophysics.
The environment of AGN provides a fundamental test for a variety of AGN triggers (Haines et al., 2012; ApJ, 754,97). For example, if cold gas or galaxy mergers are required for nuclear activity then AGN should be distributed primarily in low mass groups and the filaments that feed into clusters. On the other hand, if nuclear activity is fuelled by hot gas we would expect AGN to be most prevalent in the core regions of groups and clusters. If AGN activity is a stochastic phenomenon, unrelated to environment, then we would expect the distribution of AGN to mirror that of star forming galaxies (their most common host galaxy). So far, the environment of AGN has been investigated in limited scope due to the expense of obtaining spectra and identifying AGN over wide areas. To solve this lack of data, the 1000-fibre-fed multi-object spectrograph (WEAVE) will be placed on the 4.2m William Herschel Telescope in La Palma in 2020. The forthcoming UK-led WEAVE-LOFAR (W-L) survey will use this instrument to gather more than a million spectra of active galaxies, including AGN. In this project you will use W-L to identify the precise environmental history of different types of AGN.
In our Universe structure forms hierarchically, with small objects merging to make larger ones. The end state of this process are the largest bound structures in the Universe, giant clusters of thousands of galaxies. These enormous objects are used as the testing ground for theories of galaxy formation and evolution because of their highly complex environment and long history. In this project we aim to catch these giants prior to and in the process of formation, using deep observations to identify galaxies which will eventually form galaxy clusters by the present day. This will allow us to answer such questions as how important a large dark matter halo is to the physics of galaxy transformation and what is the connection between large scale environment and local galaxy formation. To do this we will couple the latest generation of large astrophysical simulations of the Universe to our ongoing deep observation programme, using insights obtained from the full evolutionary history contained within the simulations to shed light on our observational data. We require a student with interests both in high performance computing and large observational programmes.
It is now quite well established that the distinct components of galaxies -- primarily their bulges and disks -- have different stories to tell about the evolution of these systems. To study these components separately in order to learn how they formed, we need detailed spectral mapping across the entire face of each galaxy, which can now be obtained using integral field unit (IFU) spectrographs. The World-leading project to do this is the Mapping Nearby Galaxies at APO (MaNGA) programme, which is one of the components of the on-going Sloan Digital Sky Survey. MaNGA will produce a huge IFU spectral data set for 10,000 galaxies, allowing their kinematics and chemical properties to be studied in unprecedented detail. As members of this elite programme, we have complete access to all the data, and at this stage in the project we can play a key role in shaping the over-all science programme. PhD students involved in this project will have the opportunity to work with data of a quantity and quality that has never been obtained before, and will interact with the leading scientists in this field from all over the World, to establish their own longer-term research careers.
Galaxy clusters consist of hundreds of galaxies embedded in a massive dark matter halo and a hot, dilute atmosphere that emits X-rays. The hot gas atmosphere captures the energy from major evolutionary events in the lives of these clusters, such as jetted outbursts from their central supermassive black hole and massive mergers with neighbouring clusters. This energy is dissipated through vast shocks, cold fronts, giant cavities, sound waves and turbulent eddies, which are imprinted on the hot atmosphere. The subarcsecond spatial resolution of NASA's Chandra X-ray satellite can resolve the properties of these detailed structures, and even probe the gas gravitationally captured by the supermassive black hole in nearby galaxies. The ALMA sub-mm observatory then reveals the impact of these energetic events on galaxy growth by mapping the structure of star-forming cold gas clouds throughout the universe. This project will combine Chandra observations of black hole activity and mergers with ALMA observations of cold gas flows in the host galaxies to understand how these mechanisms transform galaxies over cosmic time.
400 million years after the Big Bang, the Universe appeared dark and empty as it slowly expanded. Suddenly the first stars formed, lighting up the Universe and forming the galaxies we see today. We have few observations of this era and it makes up over a billion year gap in our knowledge. Theories show that this is the era when stars were born with up to a hundred times the mass of our Sun, and the first black holes began to appear. These first objects gave out heat and light, making bubbles in the surrounding hydrogen gas that we will observe for the first time using radio telescopes. How these bubbles are shaped and how they grow will tell us how those first stars and black holes were born, lived and died. Searching for this signal is challenging since we don’t know what it looks like and when we tune in to the radio waves, we also detect signals from everything from nearby exploding stars to mobile phones. This noise covers the first stars signal a 1000 times over, making the search for an unknown signal a true needle-in-a-haystack challenge. I have developed methods essential for removing that noise and finding that first star’s signal, even despite not knowing what it looks like. Using my work to design the SKA, an array of millions of antennas in Australia, we will make observations stretching back into the Dark Ages of our Universe, creating a movie of our Universe growing up over a billion years. This project will involve working with real data from an SKA pathfinder called LOFAR. We will use techniques to remove the overwhelming amount of noise on top of this tiny signal, and extract everything we can about the astrophysics, using machine learning and Bayesian statistics. This project is suitable for someone who wants to get straight into the data, while using what we learn to inform the next generation facility, which is hot on our heels.