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Potential PhD Topics


PhD Supervisors

Below are listed those CAS staff who may be currently looking for PhD students.


PhD Projects

Prof. Matthew Bailes

  • No projects offered at this time

Prof. Chris Blake

  • No projects offered at this time

Prof. Jean Brodie

  • No projects offered at this time

Dr. Michelle Cluver

A.Prof. Jeff Cooke

Prof. Darren Croton

  • No projects offered at this time

A.Prof. Adam Deller

Prof. Alan Duffy

A.Prof. Deanne Fisher

Prof. Chris Fluke

Prof. Duncan Forbes

  • No projects offered at this time

Prof. Karl Glazebrook

Prof. Alister Graham

Prof. Jarrod Hurley

  • No projects offered at this time

A.Prof. Glenn Kacprzak

Prof. Virginia Kilborn

  • No projects offered at this time

Prof. Sarah Maddison

Prof. Jeremy Mould

Prof. Michael Murphy

Dr. Themiya Nanayakkara

A.Prof. Emma Ryan-Weber

  • No projects offered at this time

A. Prof. Ryan Shannon

  • No projects offered at this time

A.Prof. Edward N. Taylor


Project Descriptions

The following set of projects are subject to a competitive allocation process where only a limited number of scholarships are available:


The habitats of the nearest groups in the universe

Supervisor: Dr. Michelle Cluver


The galaxies that reside in local large scale structures provide a unique opportunity to study the most recent mass assembly in our Universe. As groups coalesce into clusters, which in turn become assimilated into superclusters, the local universe provides us with a snapshot that encodes how the baryon cycle of a galaxy is being influenced by its habitat. The closest structures provide the most complete view of the mechanisms at work, down to the lowest galaxy masses. In this project we will combine a new state-of-the-art group catalogue from the 2MASS Redshift Survey with stellar mass and star formation measures from the WISE mid-infrared survey, to study how galaxies and groups are pre-processed in the southern large scale structure region spanning the Pavo-Indus Supercluster and the Eridanus and Fornax Clusters.

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Imaging and modelling the aftermath of gravitational wave mergers

Supervisor: A.Prof. Adam Deller

This project aims to capitalise on the dawn of the era of gravitational wave astronomy by studying the radio afterglows that result from gravitational wave merger events in minute detail.  When two compact objects (one neutron star plus a second neutron star or a black hole) merge, a burst of gravitational wave emission is released, and a violent outflow is launched that can lead to pan-chromatic electromagnetic emission.  By studying the radio emission of the outflowing material, we can determine both the characteristics of the outflowing material, and the viewpoint from which we are seeing the system.  Twin inputs are required: 1) ultra-high resolution radio images obtained with intercontinental radio interferometers, and 2) highly sophisticated computational models of the merger.  To date, this has been performed for just one system, the famous NS-NS merger GW170817, for which our team showed that the merger launched a powerful and narrowly collimated jet of material (Mooley, Deller, et al., Nature, 2018).  In the near future, as LIGO/Virgo detects many more NS mergers, we anticipate applying these techniques to an increasing sample of systems, recovering information about the merger events that cannot be obtained from the gravitational wave data alone and also improving on "standard siren" measurements of the expansion of the Universe.   The successful candidate will work with A/Prof Adam Deller and an ARC-funded postdoctoral researcher, along with international project partners at Caltech and Tel Aviv University, focusing on the comparison between radio interferometry data and hydrodynamical models to extract physical parameters of the merger afterglow.


Image caption: Model (left) and VLBI data (right) of the radio afterglow of the NS-NS merger event GW170817 (Mooley, Deller, et al., Nature, 2018).  By comparing the VLBI data to a range of hydrodynamical and analytic models, we were able to constrain the viewing geometry of the system and the jet parameters.

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Dark Matter Visible

Supervisors: Prof. Alan Duffy

There exists an unknown invisible mass in the Universe, outweighing everything we can see five times over. This mysterious substance is called Dark Matter, non-interaction with electromagnetism makes it both challenging to directly observe or detection as it is therefore nearly collisionless. Astronomical observations have confirmed the large-scale distribution of dark matter but to determine the particle nature will require measurements on light year scales or event direct detection of the particle itself. Confirming the nature of this new particle is one of the greatest scientific endeavours of our century. Swinburne is part of SABRE, a new experiment one-kilometre underground at a gold mine in Stawell, which will attempt to detect this dark matter.

This project will use high-resolution simulations of Milky Way-like galaxies to explore the potential properties of the dark matter near the Sun's orbit and which SABRE may detect. It will also generate distributions that can inform indirect detection efforts such microlensing, self-annihilation signals and perturbations to the visible sector. A series of zoom-in hydrodynamical simulations of Milky Way type galaxies have been created on the largest supercomputers in the world. Along with these simulations is a new technique to be developed in collaboration with Caltech’s Prof Phil F Hopkins on a more robust numerical estimate for small scale dark matter structures. This research efforts will be used to generate an expectation for the range of dark matter distributions that can inform direct detection experiments. A background/masters in numerical simulations (esp. Gadget/GIZMO) is an advantage as is experience in Python or C.

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Simulating Warm Dark Matter

Supervisors: Prof. Alan Duffy

The current dark matter paradigm has been incredibly successful in creating the large-scale structure of the universe we see around us, with simulations explaining the distribution of galaxies across cosmic distances. If you zoom in on smaller scales the simple ‘cold dark matter’ model of a massive particle that has effectively no thermal velocity begins to struggle to explain what we see. From cusp vs core tensions of the inner densities to the missing satellite problem there would appear to be only two options: complex astrophysical processes dramatically alter the dark matter structure on small scales, and / or the dark matter is in fact not perfectly collisionless and ‘cold’, instead it is either self-interacting or has some internal thermal velocity – known as warm dark matter.

Creating the simulations to explore warm dark matter are fraught with numerical instabilities which cause immense difficulties in testing this theory. In this PhD we will explore a new, more rigorous mathematical framing of the theory that will allow us to finally test this highly compelling extension of the dark matter paradigm. Experience in mathematical modelling, python / C / C++ will be advantageous.

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The Deeper, Wider, Faster program: Discovering the fastest bursts in the Universe

Supervisor: A.Prof. Jeff Cooke


The Swinburne-led Deeper, Wider, Faster (DWF) program is the first all-wavelength program designed to detect and rapidly follow up the fastest transients in the Universe, such fast radio bursts, supernova shock breakouts, all types of gamma-ray bursts, kilonovae, flare stars, and others, including the discovery of unknown classes.  DWF is the world's largest collaboration of telescopes, with over 60 major observatories on every continent and in space.  DWF coordinates wide-field radio through gamma-ray telescopes, such as Parkes, ASKAP, MeerKAT (radio), the South Pole Telescope (mm), CTIO DECam, Subaru Hyper-SuprimeCam (optical), NASA Swift, HXMT (X-ray), and HESS (gamma-ray), to observe the same target fields at the same time and processes the data in real-time either at the telescopes or using the Swinburne OzSTAR supercomputer.  Transients are identified within minutes of their outbursts in our Mission Control room at Swinburne that incorporates leading-edge data visualisation technology.  The fast identification of the transients enables rapid-response spectroscopic and imaging follow up observations before the events fade using the world’s largest telescopes, such as Keck in Hawaii, the VLT and Gemini-South in Chile, SALT in South Africa, the AAT optical and ATCA radio telescopes in Australia.  Finally, our network of over thirty 1-2 metre-class telescopes worldwide provide simultaneous and/or follow-up imaging and spectroscopy to monitor the events.

The student will participate in the DWF coordinated observing runs and help analyse the data to produce leading transient science.  Depending on the interests and experience of the student, the project will involve (1) developing techniques to search the deep optical data to investigate known fast transients and potentially discover new classes of transients, (2) cross-matching multi-wavelength (radio, optical, UV, x-ray, and gamma-ray) data to extend our knowledge of fast transients and new event behaviour, (3) progressing real-time fast transient identification and predictive capabilities using machine and deep learning techniques, and (4) enhancing and accelerating transient discovery by progressing data visualisation and data sonification techniques, including virtual reality and augmented reality analyses.

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Outflows and feeedback in extreme star forming galaxies

Supervisor: A.Prof. Deanne Fisher


In this project, the student will work with data from the new, cutting edge instrument on Keck telescope to study the gas flows in extreme star forming galaxies. Shortly (10-50 Myr) after stars form a small fraction of them will explode in violent supernova events. Supernovae, as well as intense radiation from young stars, inject energy and momentum into the surrounding gas. This arrests future star formation and provides outward pressure preventing galaxies from collapsing under their own gravity. We call this process “stellar feedback”. Without stellar feedback cosmological simulations cannot explain bulk properties of galaxies. Measuring the gas flows that come from these supernovae is therefore a critical aspect of galaxy evolution, which in the past has been extremely difficult. However, with the recent commissioning of the Keck Cosmic Web Imager we can now make point-to-point measurements of gas flows within galaxies. Over the past year our team has been awarded a significant amount of time with Keck, and has just completed a successful year of observations. The data from this cutting edge instrument is in-hand and ready to be analyzed for this project. The student would join a well developed team and survey. There are multiple Swinburne PhD students already on this project, a dedicated postdoc starting in 2021, and a current set of collaborators in US, Chile, China and Australia. The project is also part of Astro3D programs, which provides the student with a large number of opportunities for collaboration and engagement across Australia.

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Cyber-human discovery systems for petascale astronomy

Supervisor: Prof. Christopher Fluke


The immensity of data available to modern and future astronomers demands new approaches and techniques for analysis and visualisation. Astronomers are already turning to automated processing using dedicated high-performance computing resources and advanced data archives, supported by machine learning and artificial intelligence. Within this Petascale Astronomy Era, the ability to visualise data will still be crucial for quality control, decision-making, and to support new discoveries. This project addresses the question "How do humans and machines work together most effectively to maximise the scientific return from data in astronomy?" Cyber-human discovery systems combine human-centred visualisation with automated data mining, through strategies such as machine learning and artificial intelligence. At different stages of a discovery process, the amount of work performed by humans and machines can vary -- but this requires an increased understanding of what astronomers actually do! In this research, you will provide this understanding for a number of astronomical contexts, and create new solutions to support highly customisable, dynamic, visualisation solutions that will sense and enhance the capabilities of humans working closely with machines. This project will make use of Swinburne University's Advanced Visualisation facilities - the Enhanced Virtual Reality Theatre and the Swinburne Discovery Wall - and the OzSTAR Supercomputer. This project will suit a student with interests in data-intensive discovery, human-computer interaction, or human-machine interfaces. Relevant applications from astronomy and Earth observation programs will be selected, offering opportunities for students to collaborate with other researchers in the Centre for Astrophysics and Supercomputing, and potentially with industry partners.

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Gravitational lensing and cosmology with machine learning

Supervisors: Prof. Karl Glazebrook and Dr. Colin Jacobs


Strong gravitational lens systems are finding increasing use in the study of cosmology, as well as in dark matter and galaxy evolution science. Recently, most discovery of these rare objects has using used state of the art machine learning techniques such as deep neural networks (see Jacobs et al 2019). Many opportunities exist for accelerating this discovery and the science it enables, by extending the technique to new classes of lenses and sources of data. For this project the student will model gravitational lenses using modern codes and explore the limitations of an AI's ability to discern lensing across a broad range of simulations. Unsolved problems include the efficient discovery of super-rare lenses (such as those with two lensed sources or lensed transients), and discoveries in more challenging conditions such as in imaging taken in a single band. The effect of simplifying assumptions baked into lens models is not yet quantified. Investigating this, and determining the optimal network architectures and training strategies to make these valuable new discoveries is a key outcome of the project. During the project the student would develop skills in theory (lens modelling), observational astronomy (mining astronomical surveys, follow up observations) and computation (developing machine learning algorithms on the Swinburne supercomputer). Depending on the student's interest, this project could focus more heavily on the science enabled by strong lensing (e.g. constraining cosmological parameters or dark matter) or on exploring and developing the machine learning techniques. Students will benefit from membership in the ARC Centre of Excellence in All-Sky Astrophysics in 3 Dimensions (ASTRO3D). The possibility also exists for co-supervision from computer science faculty. Image: Vegetti et al. 2020

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The missing population of intermediate mass black holes

Artists's impression of a black hole. Credit: Gabriel Perez Diaz.

Supervisor: Prof. Alister Graham

There is a largely-missing population of intermediate-mass black holes (IMBHs) with masses higher than that formed by single stars today (Mbh=1.4 to 120 MSun) and less massive than the supermassive black holes (SMBHs: 105—1010 MSun) known to reside at the centres of big galaxies.  Not surprisingly, astronomers around the world are hotly pursuing the much-anticipated discovery of IMBHs.  This thesis will involve several interconnected projects involving telescope and satellite image analysis and statistical techniques.  Improved methods for estimating both IMBH and SMBH masses will be developed and applied, with ties to the upcoming Large Synoptic Survey Telescope expected.  The coexistence of these massive black holes in dense, compact star clusters at the centres of galaxies is also expected to be a source gravitational radiation detectable by the planned eLISA satellite, for which updated predictions will be made.

Students will benefit from membership in the ARC Centre of Excellence for Gravitational Wave Discovery, OzGrav.

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Artist's impression of a black hole. Credit: James Josephides.
Galaxy Structure and massive black holes

Supervisors: Prof. Alister Graham

This project will explore how stars are distributed in galaxy images obtained from both ground-based telescopes and satellites such as Hubble and Spitzer. The structure of galaxies reveals much about how they formed, how they are connected with one another and also with the massive black holes that reside in their cores. A feeling for the type of research done with Prof. Graham can be seen in his Press Releases.

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Understanding gas flows in and around galaxies

Supervisors: A.Prof. Glenn Kacprzak and Prof. Michael Murphy

Ever wonder why some galaxies form stars while others do not? Or where does all the fuel for star-formation come from and what regulates it? The evolution of galaxies is intimately tied to their gas cycles - the gas accretion, star formation, stellar death and gas expulsion. As galaxies evolve, their gas cycles (known as feedback), give rise to an extended gaseous halo surrounding galaxies. Understanding how feedback works has become recognized as THE critical unknown process missing to fully understand galaxy evolution. Therefore, gaseous galaxy halos are the key astrophysical laboratories harbouring the detailed physics of how galactic feedback governs galaxy evolution. Observationally, galaxy halos are studied with great sensitivity using quasar absorption lines. Imprinted on the quasar spectrum are the motions, chemical content, density, and temperature of the gas. These absorption signatures provide details that are unobtainable using any other method of observation. Here, the student will join an international collaboration and will examine how the host galaxy properties are linked to their circumgalactic gas properties using Hubble Space Telescope and Keck Telescope data.

Cool gas (green) from cosmic filaments accretes onto the galaxy, which drives its rotation and controls the rate at which it forms stars. Star formation and supernovae expel gas back into the circumgalatic medium (purple). Background quasars are used to study these gas flows around galaxies.

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Testing planetesimal collision models with debris disk observations

Supervisors: Prof. Sarah Maddison

Testing planetesimal collision models with debris disk observations: Planets form through the collisions of asteroid-like bodies. The only way to see these large bodies is via the dust grains they produce via collisions. The student will join an international team conducting the PLATYPUS survey with the Australia Telescope Compact Array to study intrinsic properties of debris disks and test predictions of collisional models of planetesimals.

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Ph.D. Project Title

Supervisors: Prof. Jeremy Mould


The Tully-Fisher (TF) relation is an empirical relation between the luminosity and rotational velocity of spiral galaxies. As a standard candle relation, the TF relation plays an important role in measuring redshift-independent distances of spiral galaxies and estimating the difference between the motion of galaxies and the Hubble flow. These "peculiar velocities" are a tracer of luminous and dark matter and will be used to map cosmic flows. The WALLABY survey https://www.atnf.csiro.au/research/WALLABY/ will map the sky south of declination +30. The thesis will measure TF distances to thousands of low redshift galaxies and will contribute in a significant way to the accurate measurement of the Hubble expansion of the Universe.

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Deconstructing observed spectral features of galaxies using machine learning techniques

Supervisors: Dr. Themiya Nanayakkara , Dr. Colin Jacobs , and Prof. Karl Glazebrook


Galaxy spectra are analogous to a human fingerprint or to DNA of living organisms. Spectral features of galaxies contain a wealth of information about the nature of the stars and gas in galaxies. Most of our understanding of galaxies in the observed universe has been obtained by dissecting these spectral features and comparing with theoretical models. However, there are large shortcomings in current modelling of certain emission lines that are expected to originate from a variety of sources. This project will combine state-of-the-art theoretical models, machine learning techniques, and deep observations of the early Universe from novel ground based instruments such as VLT/MUSE, Keck/KCWI and future space based telescopes such as the James Webb Space Telescope. In this project, stellar atmospheres with photoionisation and 3D radiative transfer codes will be used to generate a suite of observed spectral features of galaxies. By using machine learning techniques on the developed models, emission features that are crucial to constrain the far-UV ionising continuum of the stars and the nature and geometry of the gas in galaxies will be identified. Then these predictions will be used on observed galaxy data to probe the nature of galaxies in the very early Universe. This will be a timely project that will combine the wealth of ground based public data that are being made available by current deep spectroscopic surveys of the Universe, with future observations from the James Webb Space Telescope which is expected to be launched in 2021.

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How does dark matter shape the lives of galaxies?

Supervisor: A.Prof. Edward N Taylor

The big question at the heart of this project is: how do the global properties of a galaxy (e.g. its size, shape, star formation rate, etc) connect to the properties of the larger dark matter halo that it lives in? The only way to really answer this question is observationally, and the best observational avenue to measuring galaxy-scale dark matter halos is by exploiting the physical phenomenon of gravitational lensing. The problem is that the effect is subtle, and we have to combine (or 'stack') weak lensing measurements of many hundreds or thousands of individual galaxy lenses in order to extract a meaningful signal. In this way, what we can do is to measure the mean halo properties, averaged over some set of (hopefully) similar galaxies.

The basic idea with this project will be to explore variations in halo masses, shapes, and sizes, as a function of galaxy properties. We want to answer questions like: all else being equal, do star-forming galaxies live in more or less massive halos than non-star-forming galaxies? What about disks versus ellipticals? Or as a function of galaxy radius? The key to this project is being clever in how we identify and construct the lensing galaxy samples: we will start by using existing data from the Sloan Digital Sky Survey, and quickly move to using new data from the Taipan galaxy survey. This will increase the effective size of our lensing sample by 4 and then by 10 compared to what is possible now, which translates directly into a better ability to split lens samples according to galaxy mass, size, shape, etc. In particular, by showing what galaxy properties correlate with halo mass, we can shed new light on the role that the dark matter halo plays in shaping the lives of galaxies.

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