The confirmed exoplanets that have been discovered in the last decade represent almost 90% of the total exoplanet population known up to date. Most of those planets were detected either by transit or radial velocity techniques. Understanding the different planetary systems architectures that has been observed represents a huge challenge for planet formation theory. Even though there have been great improvements in planet formation modeling, many questions need to be answered. In this project, the aim is that the student learns how to use N-body simulations to link planet formation with the confirmed exoplanet sample that had been discovered mostly by transit and radial velocity techniques and characterize observational biases that can lead us to new exoplanet discoveries.

The UIR bands are the discrete IR emission features from various astronomical sources that that have puzzled astronomers and astrophysicists for the past 40 years. Polycyclic Aromatic Hydrocarbons (PAHs), a group of organic molecules are thought to be the emitters of these UIR bands. However, finding the exact form of PAH responsible for the UIR bands still remains a major challenge. Fullerene (C₆₀) has been identified in the ISM through its infrared bands using the Spitzer Space Telescope, and observations have revealed that the abundance of C₆₀ increases rapidly while the abundance of PAHs decreases close to stars. The photochemical fragmentation and isomerization processes under the influence of the strong UV radiation field could provide a clue to the identification. Dedicated laboratory experiments to study the photo-fragmentation of PAH under controlled conditions are imperative to understand the fate of molecules due to such phenomenon taking place in space. The student will be introduced to laboratory astrophysics; gain hands-on experience with the i-PoP experimental setup housed at the Laboratory for Astrophysics, Leiden Observatory; learn to carry out an experiment with a PAH that is a fragment of C₆₀; obtain, analyze and interpret the data to experience the scientific findings, and put the results into the wider astronomical perspective.

Recently, the Moon has once again become the focus of future missions; both for space agencies and commercial companies. For example, we recently witnessed the successful launch and completion of Artemis 1, paving the way for the future Artemis and other lunar missions. We have also had the successful launch of the first commercial mission, the Japanese company ispace's M1 mission. With many of these missions targeting the south polar region of the Moon, it is essential to characterise the geology and the features/areas of interest that could be sampled. Therefore, it is key to map geological features such as boulders, rocky craters, and rock exposures, which are not only of interest for sampling but may also be a hazard for landing. This project will aid in understanding the context of future landing sites on the Moon, specifically those of Artemis 3 and Chang’e 7. The candidate will geologically map features around the south polar region, covering future candidate landing sites, yielding invaluable information about features which will give geological context to the south polar region.

Stars are formed from the gravitational collapse of cold and dust-rich clouds of molecular hydrogen. The earliest stages of the collapse quickly produce a protostar surrounded by a rotating disk and remnant envelope of dust and gas, which provide the raw materials for further growth ("accretion") of the protostar. Many protostars also launch powerful outflows extending far into their surroundings, which are likely directly connected to the accretion processes occuring near the outflow launching point in the disk. The recently launched James Webb Space Telescope (JWST) allows us to study in unprecedented detail the faint infrared emission lines from molecules in the warm accreting gas close to the protostar and hot regions of the outflows. The LEAPS student will thus analyze new JWST spectra of a young protostar, and model the strength of emission from molecules. This will allow determination of the physical conditions in the accreting gas and outflows, which are essential to constraining star formation theory.

Our group recently discovered a new supernova remnant (SNR) around the Calvera pulsar (Arias et al. 2022) in the LOFAR Two-Metre Sky Survey (LoTSS) data. Two groups have independently confirmed that the source is indeed a SNR from its gamma-ray emission (Araya 2022, Xin & Guo 2022). This remnant is unusual because it is believed to be in the Galactic halo. We recently obtained LOFAR Low-Band Antenna observations to complement the high-band LoTSS observations. This LEAPS project will use these LBA data with the aim to characterise the Calvera SNR radio spectral index, as well as make a resolved spectral index map of the source. This will allow us to probe SNR evolution in a relatively new parameter space: the hot, ionised, low-density gas of the Galactic halo.

Interstellar dust plays an essential role in the life cycle of stars and galaxies. Although well studied, it is not clear how dust survives in the harsh environment of the galactic interstellar medium (ISM). The chemical composition, lattice structure and size distribution of the dust grains are important properties that show us the evolution of these grains as they travel through the ISM. Dust absorbs and scatters light from stars. We can study the dust properties by observing dust extinction features in the spectra of these stars that are used as a background light. By studying a broad wavelength range, we can put firm constraints on these properties. Especially the X-rays and the infrared provide specific spectral features of dust that reveal the lattice structure, chemical composition, and grain size. However, the results from observations at these wavelengths do not always match up. For instance, dust in the X-rays appears more crystalline than in the infrared. This project aims to compare the results from the X-rays and the infrared by observing the same sightline at both wavelengths. The LEAPS researcher will explore the databases of the Chandra X-ray Observatory, XMM-Newton and Spitzer Space Telescope for suitable candidates and will analyze the available spectra to determine the dust properties along these sightlines. Experience with a computer programming language is required. In particular, experience with Python will be a benefit.

Strong gravitational lensing occurs when the light from a distant galaxy is deflected due to the space-time curvature that is caused by another galaxy along the line of sight. As a result of this phenomenon, the foreground galaxy acts like a lens, and can produce multiple magnified images of the background galaxy. This phenomenon is quite a rare event, with one gravitational lens found in about a thousand galaxies observed which makes their identification from visual inspection both time consuming and prone to incompleteness as the parent population that needs inspecting tends to be of order 10^4 galaxies. The recent studies using Artificial Intelligence (AI) techniques have tackled this problem by providing algorithms that can detect strong lens systems quickly. However, the selected set of lens candidates by these algorithms is not pure and contains many non-lens samples. This project is aimed to provide high level of genuine lens candidates in our selected lens candidates. Beside the quality of detecting algorithms, we expect the purity of the selected lens candidates to be also affected by the properties of the training data. The selected candidate will be working with a set of already developed AI algorithms (which are based on deep learning) and is expected to study the properties of the training data and their effect on the completeness and purity of selected lens candidates.

The mass of a star is the principle factor that governs its luminosity, colour, lifetime and evolutionary fate. The initial mass function (hereafter IMF) is the empirical distribution of masses for a population of stars and is a fundamental measure of star formation. To date, no convincing evidence for variations in the IMFs of different star clusters has been found. This is surprising since the star formation process, and therefore the IMF, is expected to vary with environmental conditions. Gaia is a European Space Agency space telescope that was launched in 2013 with the main objective of studying the structure, origin, and evolution of the Milky Way. Gaia was designed for astrometry, and has precisely measured the distances, positions and proper motions of over a billion sources. It has become almost a cliché that Gaia is revolutionising our understanding of star formation, from the small-scale dynamics of individual stars to large scale structures like the Radcliffe wave. In this project, the student will utilise the unprecedented precision of Gaia Data Release 3 to probe the IMF of over 500 star clusters in the solar neighbourhood. The main goal of the project would be to determine whether environmental conditions have a systemic effect on the star formation process that are detectable through variations in the IMF. Demonstrating systematic variations in the IMF due to environmental conditions would be pivotal in our understanding of the star formation process. Some prior experience with Python is desirable though not a strict requirement.

All modern star formation theories point out that turbulence in the molecular gas plays a major role in star formation in the interstellar medium (ISM). Understanding the nature of this turbulence is also crucial for the large scale evolution of galaxies. However, direct measurements of the turbulent properties of the gas are hard to come by, because they require excellent spatial resolution and targeting multiple chemical species that can trace the gas at different densities in a molecular cloud. This project will utilize high angular resolution ALMA data of different molecular gas tracers (CO, HCN, HCO⁺) in the star-forming complex N55 in the Large Magellanic Cloud (LMC). The project will involve a mix of observational and theoretical techniques to measure and quantify turbulence in N55. The expected end result on the strength and nature of turbulence will allow a measurement and comparison of turbulence in molecular gas at low metallicities for the first time. Programming skills with python (or an equivalent scientific programming language) and some background in astrophysics are desirable.

The launch of the Solar Orbiter and Parker Solar Probe missions by ESA and NASA, in 2020 and 2018 respectively, opened new opportunities to study our closest star, the Sun. We are now able to study how the Sun impacts the whole solar system via the continuous flow of matter escaping the solar atmosphere, known as 'the solar wind', in exquisite detail. Importantly, we are now in a position to understand what are the drivers of the punctual perturbations (such as 'switchbacks' and short radio bursts) of the solar wind which are linked to solar activity. Both spacecraft are providing in-situ measurements of the solar wind at distances very close to the Sun, with Solar Orbiter also providing imaging of the solar surface and solar atmosphere (known as the corona) from the closest vantage point ever achieved. With these high-precision observations, we can now link phenomena happening at the Sun (such as granulation, campfires, jets) to perturbations of the solar wind measured close to the Sun by the spacecraft. As a LEAPS student you will analyse data from different instruments on board Solar Orbiter, including high-resolution solar imaging data from the Extreme Ultraviolet Imager (EUI) instrument and in-situ diagnostics of the plasma and particles in the inner solar system. Your primary goal will be to identify which features in the solar corona are good candidate sources of the various perturbations of the solar wind. This project will be performed in collaboration with four scientists who are involved in the operations and data analysis of the Solar Orbiter mission.

Hydrodynamical simulations of galaxies ideally require simultaneous computation of dynamics as well as non-equilibrium chemistry. In many cases, non-equilibrium chemistry dominates the overall computational cost, by quite a large margin. It has been shown that chemical evaluations in neural networks can take much less time than conventional numerical solutions (e.g., Richings et al. 2014) for radiative transfer problems. The initial purpose of this project is to demonstrate the advantages of using neural networks over conventional numerical chemistry solvers through comparison of chemical evolution results in hydrodynamic simulations of the galactic disk. If time allows, as an extension of the above project, the student can also produce line and dust intensity maps using both chemical network methods and the radiative transfer code: the final step is then to compare these intensity maps produced by different methods on the simulation of the gas disk around supermassive black hole. Basic python skills are required for doing this project.