Dissertation topics suggested by the teacher.
Available thesis projects for the First Cycle Degrees in Astronomy or Physics
1) Testing a dynamical void finder on cosmological simulations
Goal: test and validate a new dynamical void finder on cosmological simulations, comparing its performance with standard void detection algorithms.
Prospects:
- learn how to analyse cosmological simulations
- compare different void detection algorithms
- contribute to the development and validation of tools within CosmoBolognaLib
- acquire numerical skills in C++ and Python useful for both scientific and non-scientific applications
Requirements:
- scientific skills: medium
- computing skills: medium (C++/Python)
2) Measuring the two-point correlation function of galaxy clusters
Goal: measure the two-point correlation function of galaxy clusters using publicly available catalogues, and compare the results with theoretical expectations.
Prospects:
- learn how to analyse large-scale structure data
- measure clustering statistics of galaxy clusters
- become familiar with standard cosmological analysis tools
- acquire numerical skills in C++ and Python useful for scientific data analysis
Requirements:
- scientific skills: high
- computing skills: medium (C++/Python)
3) Emulating cosmological observables
Goal: train deep neaural networks to emulate cosmological probe observables.
Prospects:
- gain experience with deep learning techniques
- acquire advanced cosmological knowledge, useful for the Master’s thesis
- contribute to the optimisation of CosmoBolognaLib tools
- develop advanced programming skills in C++ and Python, useful in many contexts
Requirements:
- scientific skills: medium
- computing skills: medium (C++/Python)
4) A graphical web interface for the CosmoBolognaLib
Goal: implement a graphical web interface for CosmoBolognaLib.
Prospects:
- learn how to implement graphical web interfaces
- develop new cosmological tools for general use
- become familiar with Python, one of the most widely used programming languages
Requirements:
- scientific skills: low
- computing skills: medium (Python)
Available thesis projects for the Second Cycle Degree in Astrophysics and Cosmology
1) Combining high- and low-redshift cosmological probes
Goal: collect large cosmological data sets from different probes and implement numerical tools to combine them.
Prospects:
- acquire advanced knowledge of different cosmological probes
- develop advanced statistical skills
- develop strong programming skills in C++ and Python
- become familiar with modern data analysis techniques, useful in both scientific and non-scientific contexts
- contribute to the writing of one or more scientific publications
Requirements:
- scientific skills: high
- computing skills: medium/high (C++/Python)
2) Testing dark matter models with hydrodynamical cosmological simulations
Goal: extend recent analyses of halo abundance, radial distribution, and clustering to hydrodynamical cosmological simulations, investigating the impact of baryonic physics on the discrimination between alternative dark matter models.
Prospects:
- learn how to analyse large cosmological simulations
- study the abundance and spatial distribution of dark matter haloes and subhaloes
- measure clustering statistics of haloes and compare them across different dark matter scenarios
- investigate the impact of baryonic physics on cosmological observables
- contribute to the development and validation of cosmological analysis pipelines
Requirements:
- scientific skills: medium
- computing skills: medium
3) Cosmological forecasts with galaxy clusters for the Wide Survey Telescope
Goal: perform cosmological forecasts for a Wide Survey Telescope (WST)-like survey using mock catalogues of galaxy clusters, combining cluster number counts, lensing profiles, and cluster clustering statistics.
Prospects:
- learn how to analyse mock catalogues for future cosmological surveys
- perform cosmological forecasts using galaxy cluster statistics
- study cluster number counts, lensing profiles, and clustering measurements
- investigate the cosmological constraining power of next-generation surveys
- contribute to the development of analysis tools for future large-scale structure surveys
Requirements:
- scientific skills: high
- computing skills: medium
4) Bayesian deep neural networks to learn the properties of the Cosmic Web
Goal: exploit advanced machine learning techniques to study the Cosmic Web, bypassing standard summary statistics and directly learning from large cosmological data sets.
Prospects:
- gain expertise in deep learning techniques applied to cosmology
- develop new methods for cosmological analyses based on Bayesian neural networks
- explore the use of large language models to automate and orchestrate cosmological data analysis pipelines
Requirements:
- scientific skills: high
- computing skills: high (Python)
