Jan Forbrich, Jim Dale, Mark Thompson
Historically, Galactic and extragalactic star formation have largely been separate research fields with only indirect connections, but new observational capabilities finally allow us to study star formation in nearby galaxies at scales that can be directly compared to our Galactic neighbourhood. While local studies of star formation will remain unsurpassed in spatial resolution and sensitivity, they are inevitably limited to comparably small samples of star-forming regions. A much wider range of such regions, located in Giant Molecular Clouds (GMCs), can now be systematically observed in nearby galaxies, to test star formation relations observed in the Solar neighbourhood. In this project, we will study GMCs in the famous Andromeda galaxy (M31). We have just started a large-scale programme with the Submillimeter Array (SMA), located on Mauna Kea (Hawaii), to produce the first resolved measurements of gas and dust in GMCs beyond the Magellanic Clouds, in a parameter range inaccessible even to ALMA. This PhD project will be carried out within the framework of this observing programme and will aim to characterize star-forming regions and their host clouds using these unique observations as well as a wealth of multi-wavelength datasets. It may be possible to spend some fraction of this PhD project at the Harvard-Smithsonian Center for Astrophysics in Cambridge, Massachusetts (USA), to work with team members and SMA staff there.
Chiaki Kobayashi, Sean Ryan
What is the origin of elements? Just after the Big Bang, only H, He, Li, Be, B are formed. Heavier elements are formed in stars. Supernovae (core-collapse and Type Ia supernovae) produce the elements up to zinc. Some of the heavier elements can be produced by neutron star mergers - one of the gravitational wave sources detected by LIGO. Do they produce enough uranium? Ancient stars in the Milky Way contain the information to answer these questions. We now have 'galactic archaeology' surveys, including the Gaia satellite, that are measuring the elemental abundances of million stars. The student will study the origin of elements by comparing computational simulations to these observational data. Our hydrodynamical simulation code already includes basic physics such as star formation and feedback, and thus it is possible to predict the evolution of elemental abundances in the Milky Way. The student will also update the physics processes to understand the origin of elements and to predict the rates of gravitational wave sources.
Phil Lucas, Yi Sun, Neil Davey, Jan Forbrich
This project, a collaboration with the department of Computer Science, is based mainly on data from the VISTA VVV/VVVX surveys, the first large scale infrared exploration of the Milky Way in the time domain (co-led by Dr Lucas). The VVV and VVVX surveys observed about 1 billion stars in a large part of the Milky Way over a 5 year period, detecting several million variable stars in the near infrared. The aim of this project is to develop a machine learning method to classify the variable stars via their light curves, with the principal goal of detecting new types of variable star. No prior knowledge of machine learning is required. The method will be supplemented by more classical statistical approaches (e.g. period search tools) and multiwaveband data on star colours and motions. The datasets have already yielded many unclassifiable high amplitude variable stars and transients, typically very red, optically obscured stars likely having circumstellar matter. This project can be quite flexible given the range of different approaches and questions that can be explored. The student would also undertake observational follow-up of newly discovered unusual variables using telescopes in Chile. We expect that the methods developed can also be applied to other datasets, e.g. the YSOVAR mid-infrared project and many areas of human activity where unevenly sampled time series data are important.
Jim Dale and Martin Krause
Star formation is a critically important process and the degree to which it is regulated by feedback from stars is a long-unanswered question. This project concerns ground-breaking computer simulations of the effects of feedback from ionising radiation on star-forming regions, in particular to examine its ability to trigger or terminate further star formation.
The molecular clouds in which star clusters form are well known to be turbulent and realistic simulations of them must model the turbulence in some way. This is often done by either creating an initial turbulent velocity field and allowing it to decay, or by continuously driving turbulence artificially throughout the whole simulation volume. Neither of these options are realistic since, in reality, molecular clouds are constantly buffeted by external flows, which are likely to play a major role in maintaining their dynamic equilibrium, but which can’t penetrate the cloud interiors. Turbulence in real clouds is continually externally driven.
In this project, we will develop a new module for the state-of-the-art GANDALF astrophysical fluid dynamics code (Hubber, Rosotti & Booth 2017) which will allow turbulence to be artificially driven on the edges of model clouds only, imitating the external buffeting, but leaving the cloud interiors to evolve in a physically consistent manner.
We will use this code to examine the evolution of ionised bubbles (HII regions) driven by star clusters forming in turbulent clouds. Previous simulations by Dale, Ercolano & Bonnell (2012, 2013) and Dale (2017) found that the hot ionised gas was able to carve channels out of the clouds and escape, lowering the pressure inside the HII regions and strongly decreasing the damage done to the clouds by stellar feedback. However, these clouds were evolved with no boundary conditions of any kind, which is unphysical. We will investigate whether continuously driving turbulence at the edges of the cloud to model the effects of the external ISM confines the ionised gas and allows feedback to have a stronger influence on the evolution of the cloud.
We will compare the simulation results directly with exquisite new and future observations of real HII regions in the Large Magellanic Cloud, some of which will appear shortly in McLeod, Dale, et al, (2018).
Jim Dale, Chiaki Kobayashi
Many, if not most, simulations of molecular cloud evolution begin from highly artificial conditions. Typically an isolated object is initialised with a turbulent velocity field, or a cubic box with periodic boundaries is agitated until a steady state is reached, at which point gravity is enabled. Alternatively, a suitable object may be extracted from a larger-scale simulation, perhaps of a whole galactic disk. However, following the formation of individual stars in large-scale calculations is not possible, so the extracted cloud must be reconstructed at much higher numerical resolution. This process is likely to introduce uncertainties into the high-resolution simulation, since the density and velocity fields need to be extrapolated, but the severity and nature of these uncertainties has not been investigated in detail. The first part of this project will involve extracting model clouds from large-scale simulations and exploring different methods for re-resolving them.
The second part of the project will examine the effects of photoionising feedback from massive stars on the realistically-constructed model clouds. Earlier work by Dale, Ercolano and Bonnel (2012, 2013) found that the escape velocity of a molecular cloud has a crucial role in determining how much damage ionising feedback is able to do. However, these models used isolated and roughly spherical clouds with deep and approximately spherically symmetric gravitational potentials. We will investigate what effect the much more complex shape of the realistically-generated clouds has on the influence of radiative feedback.