The interior structure of an exoplanet leaves observable imprints on its gravitational field and tidal response, quantified by the Love numbers. These parameters describe the planet’s mass distribution, providing valuable insights into its composition and interior structure. Currently, Love numbers have been determined for only a few exoplanets, highlighting the need to expand our knowledge in this area. This project will explore the potential for measuring Love numbers with current and upcoming instruments, such as Ariel. It consists of two main components: (1) modeling planetary interiors to predict theoretical Love numbers for a range of exoplanets and (2) simulating light curves to assess how different compositions and Love numbers impact transit observations. These simulations will help determine the sensitivity required for detecting Love numbers and identify promising exoplanet candidates for observation. By assessing the capabilities of current instruments and estimating resolution requirements for future telescopes, this work aims to establish a roadmap for expanding Love number measurements in exoplanetary systems. This project combines theoretical modeling, numerical simulations, and observational planning, offering the student a comprehensive experience in planetary astrophysics and exoplanet characterization.

Silicon monoxide (SiO) masers are commonly found in the envelopes around evolved stars. The very bright, narrow spectral lines associated with SiO maser emission are used to measure the line-of-sight velocities, and even in some cases, parallaxes of the stellar hosts in which they are found, making them valuable in dynamical studies. However, many details of maser formation, especially of maser emission from the isotopologues 29SiO and 30SiO, are not understood and are only poorly constrained by observation. This is a project to image the SiO maser rings in an asymptotic giant branch (AGB) star using data from the Very Long Baseline Array (VLBA). The large separation between dishes of the VLBA allows for extremely high-resolution imaging (about 0.2 milliarcsec), where we aim to resolve individual maser spots within the envelope of an AGB star. Data for multiple maser transitions is available for this project, including transitions of the 29SiO and 30SiO isotopologues. Comparison between the formation radii of the transitions can be performed. Some of the transitions have been previously mapped in many sources, while others have never been imaged at high resolution. We aim to image both the ubiquitous and the more rare transitions, providing details of their morphology.

The discovery of absorption polarization from interstellar dust took place in the 1950s, while the first reports of emission polarization appeared in the 1980s. Over recent decades, numerous observations of dust polarization with enhanced sensitivity and resolution have been conducted. Thus, dust polarization has emerged as a valuable third observational technique, alongside photometry and spectroscopy, enabling the direct assessment of fundamental dust grain properties such as size, shape, and composition, as well as the indirect examination of cosmic magnetic fields. Theoretical models have been introduced and refined to comprehensively interpret these observations. The most widely accepted theory is Radiative Torque Alignment (RAT-A), which involves the interaction of an incident anisotropic radiation field with an irregularly-shaped dust grain. Recently, it has been discovered that rotation of dust induced by radiative torques can lead to the fragmentation of the largest grains, termed as Radiative Torque Disruption (RAT-D). Together, RAT-A and RAT-D are referred to as the RAT paradigm, which has seen the majority of its predictions supported by observational evidence. This project seeks to enhance an existing numerical model, DustPOL-py[1], specifically for the scenario of clouds illuminated by a nearby luminous source. The refined model will be used to directly compare observations of both absorption and thermal dust polarization.

The existence of a radiative zone in Jupiter’s atmosphere has been a key question for decades, significantly influencing our understanding of gas giant atmospheres. A radiative zone is characterized by energy transfer primarily through radiation, as opposed to convection, and plays a crucial role in the thermal structure and stability of the atmosphere. This zone is stable with respect to convection, resulting in a unique density stratification pattern. Understanding this layer is essential for grasping how heat is distributed within Jupiter, affecting overall dynamics, composition, and even the evolution of the planet.
Recent advances in gravitational measurements from the Juno spacecraft provide a unique opportunity to investigate the influence of stable stratification on the resulting gravitational signature of Jupiter. Stable stratification can profoundly impact atmospheric dynamics, including circulation patterns and thermal profiles. Jet streams, which are fast-flowing air currents, are significant features of Jupiter’s atmosphere. They are associated with the planet’s distinctive red and white banded structure and contribute to a unique gravitational anomaly.
This project aims to examine how different stable stratification scenarios within Jupiter’s atmosphere influence its gravitational pull, utilizing recent data from Juno to validate our findings. The student will be tasked with developing potential background density profiles that incorporate a radiative zone and calculating the resulting surface gravity signals in conjunction with jet stream patterns. By combining these elements, we aim to gain deeper insights into the complex atmospheric behavior of Jupiter and the implications for its gravitational signature. The findings of this study will contribute to the broader field of planetary science and pave the way for future research on gas giants both within our Solar System and beyond.

The Universe’s matter forms an interconnected, web-like pattern known as the Cosmic Web [1], an intricate network of clusters, filaments, walls, and vast empty voids. This web of structure, predicted by theory and confirmed through powerful surveys like SDSS [2], 2MRS [3] and soon Euclid [4], emerges from the gravitational collapse of primordial density fluctuations at the beginning of the Universe. The most striking features of this cosmic structure are filaments, with various lengths and thicknesses that host half the Universe’s mass [5]. These structures channel cold, dense gas into galaxies, fueling star formation in the early Universe and shaping the evolution of massive galaxies [6]. Not only do cosmic filaments act as conduction channels for the Universe’s matter, but they also influence the angular momentum of dark matter halos, drive galaxy disk formation, and spark turbulence and instabilities [7],[8]. Despite their vital role in cosmic evolution, the inner workings of filaments remain not fully understood, hindered by the limitations of current structure-finding algorithms and tools tracing the skeletons of these filaments. Methods such as DisPerSE [9] have pushed the boundaries of this field but still struggle with false positives and sensitivity to data variations. We therefore suggest the use of the recently introduced and interpretable machine learning toolbox termed 1-DREAM [10]–[12], which allows for the robust and stable extraction of filamentary structure within cosmological datasets for the exploration of the flow of matter surrounding cosmic filaments. At high redshift, the flow of matter within the Universe is dominated by linear expansion, while at lower redshift, the dynamics within cosmic filaments become nonlinear, marking the limit of the linear expansion regime [13]. It is therefore a great point of interest to map with greater extent that dynamics that matter undergoes during its evolution through the cosmic web. In terms of datasets, the student will be making use of the Illustris TNG simulations [14] which will subsequently be used as input for the suggested methodology. The student will also learn how to use the algorithms behind the 1-DREAM toolbox which will equip them with knowledge in structure-detection and concepts in machine learning. Finally, the student will have greater knowledge around the workings of cosmic structure formation. The main outcomes of this project would be to: (1) extract a sample of cosmic filaments from within a standard simulation cube, (2) construct a 3D model of the motion of simulation particles comprising the filaments, (3) perform a statistical study on how such motion depends on various properties of the filaments, e.g. their lengths, diameters, and proximity to clusters. [Please contact supervisors for references]

The composition of planets is determined by that of the protoplanetary discs in which they form. There has recently been a renewed interest in the inner parts of these discs since these can be probed with observations from JWST. These have suggested links between the structure of the disc and the chemistry in its inner regions, in particular the strength of water emission at colder temperatures. Several models have attempted to interpret these links in the context of "dust traps" - pressure bumps in the disc which block the inward motion expected of large dust grains which manifest as bright rings outside of dark gaps in ALMA images. These dust grains carry ices which can sublimate if the grains reach warmer temperatures and hence enrich the warm inner disc gas with molecules. However, all these models so far have assumed that the temperature in the bumps follows the smooth background temperature profile of the disc, whereas in reality radiation may penetrate better into the gaps leading to warmer gas. This could result in the more volatile ices sublimating locally at the trap and thus managing to escape the trap. The student will first work to extract temperature profiles from 2D disc models including such traps, in which the temperature structure has been calculated self-consistently. They will then implement these profiles into a python code which models the evolution of the dust and gas in a disc in order to understand how much the altered temperature structure facilitates the escaping of these molecules from the dust traps, thus changing the inner disc chemistry. This will allow us to answer questions such as 1) How efficient are dust traps at trapping different molecules? 2) How deep are the gaps which most efficiently block molecules from reaching the inner disc? 3) Is this effect important for the dust traps we can observe with ALMA?

Magnetars are neutron stars with extreme magnetic field strengths of 1 billion Tesla or more. Under the assumption of rotational energy loss through magnetic braking, they have young age estimates, typically less than 100kyr. However, their X-ray luminosity far exceeds the spin-down luminosity - the direct decay of the intense magnetic field is thought to be responsible for this discrepancy, and this likely biases the age measurement. How old are magnetars really? How much energy do they dissipate through magnetic braking versus outbursts and flares? In principle, we can obtain a constraint on the total energy output by observing their influence on the local interstellar medium. In this project, the student will search for a wind-driven nebula around a Galactic magnetar in deep near-infrared imaging from the Very Large Telescope, combined with X-ray data from XMM-Newton and the Chandra X-ray Observatory to place a new constraint on the energy dissipation processes of a magnetar. Additionally, the near-infrared imaging allows us to see deep into the dusty galactic plane. Thanks to a proper motion measurement, we can therefore also search for the magnetar’s birth site. Desirable skills: experience handling (fits) images with a programming language such as Python.

Solar flares are among the most energetic events in our solar system, with significant impacts on space weather and human technology. Recent research has identified specific signatures that may occur minutes before a flare (known as Hot Onset Precursor Events, or HOPE), opening new possibilities for flare prediction. This project will analyse over a decade of spectroscopic data from the Hinode/Extreme-ultraviolet Spectrometer (EIS) instrument to investigate these pre-flare signatures across approximately 2,000 flare events. The student will develop Python-based analysis tools to track the evolution of key spectral parameters before flares, search for systematic patterns in temperature and density changes that could serve as early warning signs, and apply statistical analysis techniques to validate potential predictive signatures. Working with real spacecraft data from the Hinode mission, the student will gain experience in solar physics research, scientific programming, and modern data analysis methods. The project will contribute significantly to our understanding of space weather prediction and builds upon recent discoveries in flare forecasting. The ideal candidate will have programming experience (preferably Python), an interest in solar physics or space weather. This research will be conducted in collaboration with solar physics experts and could have important implications for satellite operations and space exploration.

In 2030, the ExoMars Rosalind Franklin Rover (RFR) will arrive at Oxia Planum, Mars, and begin its search for the chemical signatures of life. The success of the mission is predicated on safely traversing to areas of scientific interest and utilizing the suite of instrumentation onboard the rover. Key to long term strategic planning for the RFR mission is rover traversability: what terrains are safe to drive across; what terrains or features on the landscape are potential mobility hazards; and how efficiently can the rover make it from one point to another? Unfortunately, these questions can’t always be answered by analyzing high-resolution single- and multi-band orbital imagery. And despite extensive geologic and mineralogic characterization of the landing site, key questions remain. In this project, the student will analyze in situ rover traverse data from Mars Science Laboratory Curiosity Rover and Mars 2020 Perseverance Rover. A comparison of this data with high-resolution images and machine learning generated landscape classifications in a Geographic Information Systems (GIS) software will characterise the rover terrain types Curiosity and Perseverance rovers encountered along their traverse. This will then be compared to the landscape classifications at Oxia Planum. Metrics such as the average drive distance over different terrain types, sol-to-sol drive distances, and a catalogue of rover obstacles or terrain types to be avoided will be elucidated.