This proposed research project aims to shed light on the density-velocity relation of circumnuclear discs of galaxies using HDGAS hydrodynamic simulations. By investigating the interplay between density and velocity, as influenced by various feedback parameters (i.e. wind/jet velocity and mass loading factor), we can gain valuable insights into the formation, evolution, and dynamics of galactic discs. While hydrodynamic simulations are essential for studying the underlying physical processes, comparing their results with observational data helps validate the simulations and provides a real-world context. There are some key observational results related to the density-velocity relation in the presence of AGN we can compare with HDGAS simulation: (a) Ionized Gas Dynamics: Observations have revealed that AGN activity can significantly influence the density-velocity relation of ionized gas in galaxies. AGN-driven outflows or jets can create regions of low-density, high-velocity gas, resulting in disturbed velocity fields and asymmetric density distributions. These disruptions can be observed as kinematic asymmetries, such as double-peaked or asymmetric line profiles. (b) Molecular Gas Kinematics: Studies of molecular gas, commonly traced by carbon monoxide (CO) emission, have provided insights into the density-velocity relation within AGN-hosting galaxies. Observations have shown that AGN can induce perturbations in molecular gas kinematics, leading to non-circular motions, velocity dispersions, and warps in the velocity field. These effects can be observed through CO line profiles and position-velocity diagrams.

AD Leonis is a bright M dwarf whose magnetic properties have been investigated extensively via spectropolarimetry. The latter is an observational technique that collects spectra in both unpolarised and polarised light, and is sensitive to the effects of magnetic fields on atoms present on the stellar surface. From a time series of circularly polarised spectra, we can reconstruct the shape of the large-scale magnetic field with a technique called Zeeman-Doppler imaging (ZDI), which can then be used as a boundary condition to simulate the environment and space weather around the star. Previous work revealed a strong large-scale magnetic field (1 kGauss) with a dipolar configuration that is symmetric relative to the stellar rotation axis. Recently, the magnetic field has weakened and tilted from the pole toward the equator, showing signs of an imminent polarity reversal and overall a solar-like cycle. The student will work on new near-infrared spectropolarimetric data to i) characterise the magnetic field of AD Leonis, ii) reconstruct a map of the large-scale magnetic field, and iii) simulate the stellar wind and environment surrounding the star. The project has both an observational/data analysis component, as well as a theoretical/simulation component. The output will feed back on the evolution of the large-scale magnetic field of AD Leonis and its environment, providing insights for both dynamo theories and star-planet interaction modelling.

The locations of extragalactic transients, such as supernovae, tell us much about their origins. For example, long-duration gamma-ray bursts (GRBs) are strongly biassed towards the brightest regions of their star-forming host galaxies, implying an origin from the core-collapse of the shortest-lived, most luminous, and most massive stars. The distribution of transients on and around their host galaxies can be quantified in a variety of ways. Typical measurements include the offset (the projected distance from the centre of the galaxy to the transient), and the 'fraction of light' statistic, which quantifies how biassed transients are towards (or away) from bright areas in the galaxy. Many transients are linked with stellar remnants, such as neutron stars. For example, a leading hypothesis for fast radio bursts is that they are produced by magnetars (strongly magnetised neutron stars), and some long GRB models require binary star progenitor systems, which may be similar to X-ray binaries (XRBs). A way to test these ideas is to compare the spatial distribution of stellar remnants in the Milky Way - such as magnetars and XRBs - with the distribution of extragalactic transients in their host galaxies. However, since we are embedded in the Milky Way disc, creating an external image of our Galaxy - a necessary step to perform a comparison with transient locations in other galaxies - is challenging. A potential solution lies in the Magellanic clouds. These satellite galaxies are unique because they are external to the Milky Way, and therefore viewable in their entirety, but also close enough that large populations of stellar remnants within them can be detected. Previous works have studied the distribution of massive stars in the Magellanic clouds, comparing this with the locations of supernovae in distant galaxies. However, a comparison of the distribution of stellar remnants in the Magellanic clouds, versus the environments of extragalactic transients, has yet to be carried out. This is the aim of the project, which will make particular use of Gaia data for mapping the clouds. The project will therefore provide a unique test of progenitor models for various extragalactic transients. Some experience with a programming language (e.g. python) is desirable.

The composition of planets is determined by that of the protoplanetary disks in which they form. The chemical composition of these disks is seen to be very diverse and is expected to evolve over time due to the radial motion of its constituent gas and dust components. In particular, dust grains undergo rapid inwards radial drift. As they do so they experience warmer ambient temperatures which can result in the sublimation of ice species from the grains. The temperature - and hence location - at which this desorption occurs can depend strongly on the interactions between different molecular ice species which can be parametrized through the “binding energy”. The goal of this project is to undertake simulations to explore the impact of different binding energies pertaining to pure ices, mixed (polar) ices or semi-refractory ammonium salts on the delivery of NH3 (which has so far eluded detection in JWST observations of protoplanetary disks) to the inner disk. For example, can NH3 become locked in solids and hidden from observations in the mid-IR? When in a disk's evolution are we most likely to see ammonia in the inner disk? The main simulations will be conducted using a python code following the evolution of disk gas & dust in 1D. The outputs could then be passed to a thermochemical code or slab models to make predictions for detectability of NH3 in mid-IR spectra or coupled with simple prescriptions for planet formation to understand how planetary nitrogen abundances would be affected.

The mechanism of galaxy evolution is tightly related to the growth of dark matter haloes. To investigate the invisible dark matter haloes that are hosting galaxies, we can use gravitational lensing analysis of wide-field surveys (e.g. Kilo-Degree Survey, Hyper-Suprime Cam Survey). As the surveys provide sharp galaxy images, it is possible to analyse shape distortion of distant background galaxies caused by the gravitational potential of target galaxies, which we call “gravitational lensing analysis.” By stacking the shape distortion signals from millions of galaxies, the profile of dark matter haloes of the target galaxies can be precisely measured. Among interesting galaxies, studies of dwarf galaxies provide ample knowledge about small scale properties of dark matter and galaxy-halo connection. Dwarf galaxies are tiny. Therefore, obtaining their gravitational lensing signal is a challenging task. We will apply a machine learning algorithm to select the dwarf galaxies. Eventually, we aim to constrain halo-model (profile) parameters from the measured gravitational lensing signal of dwarf galaxies.

Understanding the physical processes taking place in the Sun and being able to predict violent events such as solar flares is imperative in order to sustain safe advancements in space exploration. The Solar Orbiter, developed by ESA, offers large advancements in measurements of the solar atmosphere. The spacecraft consists of several remote-sensing instruments operating at different wavelength regimes, each specialized to measure specific properties of the solar atmosphere. In addition, the close orbit around the Sun and the coming high latitude orbit of the Solar Orbiter enabling the study of the solar poles, facilitates novel scientific analysis. On the other hand, observations in the radio regime provide very powerful diagnostics to study the solar atmosphere as they provide more direct temperature measurements of the probed plasma. The Earth-based observatory Atacama Large Millimeter/sub-millimeter Array (ALMA), consisting of about 66 antennas, provides ground-breaking measurements in the radio regime in terms of high sensitivity and angular resolution, necessary to resolve small-scale features. This project would involve identifying small-scale features in the observations of the solar atmosphere and study the correlation between signatures at millimeter wavelengths to intensity measurements at other wavelength regimes and magnetic field measurements. The addition of the plasma temperature measurements with ALMA is important in understanding the formation processes of dynamic events detected in the Solar Orbiter data, that could potentially be small solar flares. The project could include machine learning techniques for the statistical analysis and feature detection algorithms. One of the main aims would be to efficiently distinguish potential different formation processes of small-scale brightening events, which would lead to meaningful scientific publication.

Cosmic structure formation proceeds in a bottom-up manner, whereby small structures form first and then coalesce together to form more massive ones. This process is largely driven by dynamical friction, which is the result of a lagging wake of mass behind the least massive object, inducing a dragging force that removes its orbital energy and angular momentum. The efficiency of this process, and hence the timescale on which mergers occur, depends on the orbital and structural properties of the objects that are involved. Previous models used to predict merging timescales are based on analytical arguments or formulas calibrated to cosmological simulations, which have resulted in varying degrees of success. In this project, we will leverage targeted simulations that sample a broad range of possible parameter combinations – e.g. orbital energy, eccentricity, relative masses – together with machine learning techniques to explore how well previous models do, what role does numerical resolution play, and whether emulation-based models fare better than traditional approaches. Programming skills are recommended.

Star-forming regions exhibit complex physical and chemical properties. Even a single region displays extremely different gas conditions, such as density and temperature, leading to local chemical variations. While it is possible to distinguish between the different environments within star-forming regions in our Galaxy, it may not always be feasible for external galaxies. In such cases, chemical modeling plays a crucial role in accurately constraining the origin of the observed emission. The goal of the project is to build a set of chemical templates of the most common environments within star-forming regions, like shocks and protostellar cores. The student will concentrate on species that are efficiently released through prevalent mechanisms in these environments, such as sputtering and heating. In particular, the focus will be on species, e.g., CH3OH, which can be equally abundant in shocks and protostellar cores. Using the in-house gas-grain chemical code, the student will model expected chemical properties and identify which physical conditions are critical for the chemical enhancement of the selected species. The model outputs will be validated against observational data from the literature.

Solar flares are the most energetic events in our solar system, capable of impacting technology and astronauts in space. As we enter the era of Solar Orbiter and new solar missions, understanding the processes behind flares takes on renewed significance. This project leverages over a decade of solar observations from the ESA/JAXA-led Hinode spacecraft to uncover and elucidate flare acceleration and heating mechanisms. Specifically, the student will compile a database correlating plasma properties with solar images, seeking relationships between flaring magnetic structures and energy release. Depending on the progress in the first part of the project, potential extensions include deriving key plasma parameters (densities, temperatures) over flare evolution from EIS spectra; alongside incorporating data from spacecraft observing different layers of the solar atmosphere, including the recently launched Solar Orbiter. The database created through this project will enable a series of novel statistical studies to fully understand solar flares and the spectroscopic signatures across events. As we enter an era of new missions like Solar Orbiter, this project provides an essential dataset to further the study of flare heating mechanisms and space weather origins. This project will be performed in collaboration with ESA scientists who are involved in a range of state-of-the-art solar missions, including the Solar Orbiter and Hinode emission.