Foto del docente

Federico Marulli

Associate Professor

Department of Physics and Astronomy "Augusto Righi"

Academic discipline: FIS/05 Astronomy and Astrophysics

Teaching

Dissertation topics suggested by the teacher.

Available thesis projects for the First Cycle Degrees in Astronomy or Physics

 

1) Comparing alternative estimators of the two-point correlation function monopole

Goal: implement and validate alternative statistical estimators of the clustering monopole

Prospects:

  • learn how to use standard codes for cosmological analyses
  • contribute to the CosmoBolognaLib implementation
  • acquire numerical knowledges, in particular in C++ and Python, useful for both scientific and many other non-scientific activities
Requirements:
  • scientific skills: low
  • computing skills: low (C++/Python)


2) A graphic interface for the CosmoBolognaLib

Goal: build up a graphic cosmological calculator for public use

Prospects:

  • learn how to implement graphic interfaces
  • build a cosmological tool for general usage
  • familiarize with Python, one of the most popular programming languages
Requirements:
  • scientific skills: low
  • computing skills: medium (Python)


3) Restructuring the CosmoBolognaLib class Cosmology, to generalize to alternative cosmological frameworks

Goal: extend the CosmoBolognaLib class Cosmology to provide cosmological predictions for non-standard cosmologies

Prospects:

  • become one of the CosmoBolognaLib main builders
  • acquire high-level cosmological knowledges, useful for the Master Thesis
  • acquire high-level skills in C++ and Python, useful for both scientific and many other non-scientific activities
Requirements:
  • scientific skills: medium
  • computing skills: high (C++/Python)

 

Available thesis projects for the Second Cycle Degree in Astrophysics and Cosmology

 

1) Validating the Euclid clustering codes on the Flagship Simulation

Goal: test the new Euclid clustering pipeline on galaxy and cluster mock Euclid catalogues

Prospects:

  • contribute to the preparation of the ESA Euclid mission
  • become a C++ expert
  • learn how to exploit the latest numerical tools to do cosmology with next generation galaxy surveys
  • contribute to write internal Euclid reports

Requirements:

  • scientific skills: medium
  • computing skills: medium/high (C++)

 

2) Cosmological constraints from cluster number counts and clustering in Euclid

Goal: participate to the Euclid Cluster Cosmology Challenge, aimed at implementing the Euclid likelihood modules to model cluster statistics

Prospects:

  • contribute to the preparation of the ESA Euclid mission
  • become a C++/Python expert
  • learn how to exploit the latest numerical tools to do cosmology with next generation cluster surveys
  • contribute to implement new Euclid modules for cosmological analyses
  • contribute to write internal Euclid reports

Requirements:

  • scientific skills: medium
  • computing skills: high (C++/Python)

 

3) Halo Occupation Distribution model to extract cosmological constraints from small-scale clustering

Goal: implement the Halo Occupation Distribution model in the CosmoBolognaLib, and exploit it on real data

Prospects:

  • become one of the CosmoBolognaLib main builders
  • become a C++/Python expert
  • derive new cosmological constraints from galaxy clustering at small scales (possible applications to VIPERS and BOSS surveys)
  • contribute to write one or more scientific publications

Requirements:

  • scientific skills: medium
  • computing skills: high (C++/Python)

 

4) Combining high and low redshift cosmological probes

Goal: collect a large dataset of cosmological data from different probes, and implement the numerical tools to combine them

Prospects:

  • acquire high-level cosmological knowledges on different probes
  • acquire high-level statistical skills
  • become a C++/Python expert
  • familiarize with the latest data analysis techniques, useful for both scientific and many other non-scientific activities
  • contribute to write one or more scientific publications

Requirements:

  • scientific skills: high
  • computing skills: medium/high (C++/Python)

 

5) Bayesian deep neural networks to learn the properties of the Cosmic Web

Goal: exploit the latest machine learning techniques to do cosmology bypassing standard statistics

Prospects:

  • become an expert on deep learning techniques
  • develop new methods, never tested before, for cosmological analyses
  • become one of the first builders of the CosmoBolognaNet
  • start a long-term project, to be hopefully continued during the Ph.D.

Requirements:

  • scientific skills: high
  • computing skills: high (Python)