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Supervisors: Elmo Tempel, Radu S. Stoica, Rien van de Weijgaert

This project aims to detect and characterize the walls and voids in the spatial galaxy distribution using stochastic geometry and persistent topology. The first goal is to create a stochastic geometric model for the pattern of randomly distributed flattened walls, forming the cosmic web. This model will trace large walls in galaxy redshift surveys and characterize their properties, such as size, surface density, and connectivity.
The second goal is to extend this framework to detect cosmic voids, which are crucial elements of the large-scale matter distribution. These voids have evolved from primordial matter troughs and are sensitive to dark energy, dark matter, and gravity. The project aims to define voids accurately using a Bayesian stochastic geometric formalism, allowing precise statistical characterization of their volume, shape, and other aspects.
The project will use Gibbs marked point processes to model walls and voids in the spatial point distribution, suitable for pattern detection in galaxy surveys. Additionally, it will investigate the connectivity of filaments, walls, and voids in the cosmic web using algebraic and computational topology. By analyzing persistence diagrams and Betti numbers, the project will infer the hierarchical
topology of the cosmic web. Combining geometric and topological aspects with statistical characterization, the project aims to develop methods to infer cosmological constraints from the spatial galaxy distribution. This framework will be applied to real galaxy redshift surveys like DESI, 4MOST, and Euclid, aligning with ongoing and upcoming observational surveys.

Supervisors: Krista Alikas, Riho Vendt, Viktor Vabson

Water leaving radiance is a key parameter for ocean colour (OC) satellite radiometry. It is the basis for higher order products (e.g. chlorophyll a) and subsequent spatiotemporal analyses. Measurement schemes for in situ above-water radiometry are already well addressed, but in-water measurements, despite considered more accurate, still need attention. Optical laboratory facilities at Tartu observatory will be advanced to allow the characterization and calibration of in-water radiometers, assuring the traceability of a measurement and uncertainty budget derivation when moving from controlled laboratory to variable outdoor conditions. Outdoor comparisons of common radiometers in various deployment strategies, together with the development of new sensor prototype will help the community to optimize the inwater measurements. This allows producing traceable in situ measurements required for every OC satellite mission for validation, vicarious calibration and algorithm development.

Supervisor: Tiina Liimets

Symbiotic binaries represent a crucial yet shortly lived late stage in the evolution of certain low-mass stars. These systems contain a hot component accreting matter from a red giant donor, potentially leading to Type Ia supernovae and enriching the universe with essential heavy elements. Intermittent outbursts from the accretion disk, along with interacting stellar winds, produce complex, extended nebular structures characterized by diverse shapes and high-velocity jets. These nebulae provide a unique opportunity to study the history of mass loss, the forces driving various outburst events, and the mechanisms shaping their morphology—key aspects of symbiotic activity that remain poorly understood. This PhD project aims to investigate several of these nebulae in detail to address these questions. It will involve a comprehensive imaging survey of extended emission around known symbiotic binaries to better understand their formation mechanisms. Additionally, it will include in-depth studies of selected symbiotic nebulae, for example R Aquarii, CH Cyg, HM Sge, V1016 Cyg. For the project, local Estonian telescopes, as well as telescopes in other astronomical sites will be used.

Supervisors: Lea Hallik, Erko Jakobson, Margit Aun

Healthy wetlands provide numerous ecosystem services such as water purification, flood control, and carbon sequestration. Wetlands play a critical role in mitigating climate change impacts like floods and droughts. Understanding how restored wetlands respond to climate change can inform adaptation strategies for both wetlands and surrounding communities. Earth Observation (EO) data from satellites can cover vast areas quickly and repeatedly, offering a cost-effective way to monitor restoration progress across entire landscapes in a standardized way facilitating comparisons and knowledge sharing. The project will contribute to the development of standardized EO-based protocols for wetland monitoring, which can be easily adopted by other restoration projects and agencies. COPERNICUS Services provide unprecedented amount of temporally and spatially continuous data. Climate predictions are provided at various timescales from seasonal forecasts to long-term climate projections for different scenarios. Re-analysed databases such as ERA5 provide spatially continuous time-series of historic climate data. Earth Observation satellites provide long time-series of monitoring data and derived products (e.g. land cover classes, biophysical products, phenology metrics). Combining EO data with past climate information can help to identify key drivers influencing restoration success, like precipitation patterns, temperature changes, or human activities. Integrating climate projections with EO data can predict potential challenges for the restored ecosystem, allowing for proactive adaptation strategies. The combined analysis of EO and climate data can deepen our understanding of how wetlands respond to restoration efforts and climate change, providing valuable insights for future strategies of climate change adaptation.

Supervisors: Elmo Tempel, Pekka Heinämäki

This project studies Brightest Cluster Galaxies (BCGs) to understand cosmic structure formation. BCGs are extremely bright, allowing observations at high redshifts. The 4MOST survey, starting in 2026, will observe BCGs and their environments across the southern hemisphere, providing new insights into their formation within the cosmic web. BCGs are typically found at the centres of massive, relaxed galaxy clusters. The 4MOST survey aims to understand the connection between clusters and the filaments that feed them, which is crucial for BCG evolution. Galaxy clusters and groups form a continuum with no clear boundary between them. Understanding BCGs across this mass range is important. Observational dataset, combined with simulations, will help study BCGs and brightest group galaxies (BGG), their host systems, and their connection to the cosmic web. The 4MOST survey will extend the analysis to less massive groups. The upcoming 4MOST WAVES survey will significantly increase the number of observed groups and their member galaxies. Hydrodynamical simulations will provide detailed insights into BCG/BGGs and their co-evolution with host systems.
The main goal of this PhD research is to understand how BCG/BGGs and their host systems form and evolve in galaxy groups of different masses, and how these processes differ from those in galaxy clusters.

Supervisors: Shishir Sankhyayan, Elmo Tempel

This PhD project aims to understand the dynamics of the Universe’s largest structures – superclusters and voids - and their impact on large-scale matter flow. Superclusters are the largest over-densities present in the Universe, while voids are the enormous under-dense regions, almost empty at their centres. By studying these vast structures and their spatial correlation, we aim to get deeper insights into the fundamental constituents that shape the Universe, including dark matter and dark energy. Superclusters are not gravitationally bound systems, yet their gravitational potential affects how matter moves within them. Similarly, voids continuously expand, pushing matter outward and shaping the cosmic web. We will use advanced simulations and astronomical data to analyse these motions, developing physically and dynamically motivated new ways to define and measure voids and superclusters more accurately. A key goal is to explore whether the distances between superclusters and voids remain stable over time, making them potential “standard rulers” for testing cosmological models. If successful, this approach could provide a new method for measuring the Universe’s expansion and refining our understanding of dark matter and dark energy. By combining state-of-the-art computational techniques with the latest observational data, this project will help understand the role of superclusters and voids in forming large-scale structures, offering a fresh perspective on the largest building blocks of our universe.

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Supervisors: Rain Kipper, Indrek Vurm

A significant part of our knowledge about the structure of the Universe and the processes that control it relies on large-scale cosmological simulations and their validation through observations. One of the critical building blocks in these simulations is stellar feedback, i.e., the energy and momentum deposited into the interstellar medium by massive stars and supernovae, which has a significant impact on the overall evolution of galaxies. The details of feedback remains unresolved in many aspects. This doctoral thesis focuses on two of these aspects: the detailed description of the interaction between the material ejected by supernovae and the surrounding environment, and the consideration of the diversity of stellar explosions in feedback calculations. In this context, recent years have seen significant developments with the discovery of several previously unknown types of supernovae, including so-called superluminous supernovae, whose energy budget is 1-2 orders of magnitude greater than that of ordinary supernovae. Methodologically, we approach the problem on three different complementary levels: analytically, numerically (using simulations), and observationally, with the aim of achieving the most comprehensive and observation-validated quantitative picture possible.

Supervisors: Jan Pisek, Evelyn Uuemaa

The rapid erosion of biodiversity poses a significant environmental challenge. Assessing biodiversity through ecological field data encounters various challenges, particularly in gathering reliable information for large areas. There is a pressing need for operational techniques utilizing remotely sensed data to aid ecologists and decision-makers. This thesis aims to explore diversity indicators derived from optical imagery, based on the spectral variation hypothesis. According to this hypothesis, the diversity of spectral patterns across spatial grids reflects greater niche heterogeneity, facilitating coexistence among organisms. First, this thesis will identify, qualify, and compare methods for operational biodiversity monitoring using remote sensing data. The concept of spectral species has recently emerged, suggesting that spectral heterogeneity at a landscape level corresponds to distinct subspaces with similar spectral signatures. Using Estonia as a case study and available remote sensing data, this thesis work will investigate the identification of these subspaces as individual spectral entities - "spectral species." Finally, remote sensing data and identified associated drivers will be integrated and processed within a data cube and machine learning models to allow spatially predictive biodiversity modelling. The aim of this thesis is to enhance standards for biodiversity mapping using remotely sensed data and to contribute to the identification of pertinent Remotely Sensed enabled Essential Biodiversity Variables.