Bachelor's degree theses

This BSc thesis activity focuses on the analysis of lunar stellar occultation data, combining observational astronomy techniques with time-series data analysis. The student will first become familiar with software tools used for occultation predictions and observation planning. Based on these predictions, the student will participate in the preparation and execution of hands-on observation campaigns using the instrumentation available at the Radio Science and Planetary Exploration laboratory, including a 40 cm Dobson telescope and a high-sampling-rate CMOS camera. The activity will also involve the analysis of previously acquired light-curve observations retrieved from public occultation databases and repositories. The student will process these datasets to extract accurate occultation timings and characterize the associated uncertainties. In parallel, the observations collected during the dedicated campaigns will be reduced and analyzed, with particular attention to signal quality, temporal resolution, noise sources, and overall data reliability. Through this work, the student will gain experience in astronomical observations, instrumentation, time-domain data analysis, and scientific data quality assessment within the context of high-angular-resolution lunar occultation studies.

Activities:
* Learn the how to use occultation prediction software, such as Occult_v4.0 or Grazprep.
* Participate in hands-on observational campaigns for lunar stellar occultations.
* Extract lightcurves data from high-frequency imaging.
* Process the extracted lightcurves to derive precise timings of star disappearance and reappearance behind the Moon’s limb using dedicates software tools like Tangra.
* Write a technical report detailing the observational pipeline and data processing procedures

Topics: Observational Astronomy / Stellar Occultations
Tutor: Riccardo Lasagni Manghi
Uploaded: 26/05/2026

One of the main goals of every space mission to small bodies (asteroids and comets) is to characterize their interior structure. This type of information reveals clues about the formation and dynamical evolution of these celestial objects. In addition, knowing how the density is distributed under their surface can optimize the effectiveness of a deflection mission, such as NASA's DART.
 
One method to draw conclusions about small bodies' interior structures is through the so called Gravity Inversion problem, where the internal density distribution is obtained starting from the gravitational field of the body. The Least Squares Method is one of the techniques employed to achieve this process and, in general, is largely adopted in many statistical problems. It is a mathematical method that determines the best-fit quantities of a model's parameters by minimizing the sum of the squared differences between observed data and the data computed by the model itself. For this application, the model's parameters are the densities of regions describing the internal structure of the asteroid, and the data consists in its gravitational field.
 
In this BSc thesis, the student will firstly gain knowledge and proficiency with the modelling of gravitational fields, the Gravity Inversion problem and the Least Squares Method. Then, based on the gained experience, a LSM tool will be developed in Python programming language. Finally, the developed inversion technique will be tested on synthetic gravity fields generated by known heterogeneous bodies. For the last task, the student will need to familiarize with the use and manipulation of 3D asteroid models represented as polyhedrons.
 
This work places itself within the framework of ESA's missions HERA and Ramses. Where the first will characterize Didymos binary asteroid, starting its scientific activities at the end of this year. The second instead will launch in 2028 to study Apophis asteroid during its close encounter with Earth.
 
Topic: Planetary Defence / Data Analysis
Tutor: Federico Scalera
Uploaded: 15/09/2026

At Jupiter and Saturn, dense clouds of neutral and charged particles populate the circumplanetary environment. The interactions of the moons that eject such particles, Io at Jupiter and Enceladus at Saturn, are often described as Alfvénic connections between the moon and the ionosphere of its host planet. The region enclosing most of the interaction can be approximated as a flux tube, i. e. a volume delimited by a set of magnetic field lines, subject to the constraint that the magnetic flux be conserved across every cross section.

The interaction of particles travelling along flux tubes with the planetary ionospheres has been observed with a variety of instruments, including UV/IR spectrographs and imagers and in situ particle detectors, most notably by NASA’s Juno mission at Jupiter. A dedicated code‐base has been developed in this laboratory to predict flux tube electron densities and dimensions, along with their radio occultation footprints, to reveal whether space‐based radio occultations can provide a viable means of detecting and studying flux tubes under present or planned mission concepts of operations ﴾ConOps﴿. It relies on a combination of the SPICE toolkit, used to analyze spaceflight geometry and ray intersections, and the JupiterMag magnetic field package, with a substantial amount of in‐house code.

The candidate will fork the codebase and adapt it to the Saturnian environment, introducing an appropriate plasma environment and magnetic field model.

Expected Outcomes:
• Estimates of density thresholds ensuring measurability of flux tubes via radio occultations
at Saturn.
• An updated literature review on flux tubes.
• An updated codebase, capable to analyze the flux tube observability problem at Saturn.

Requirements:
• Some preliminary knowledge of Python is appreciated.
• Knowledge or interest in MagnetoHydroDynamics ﴾MHD﴿ is appreciated.
• Solid English reading skills.

Acquired skills:
• Solid Python programming.
• Usage of the SPICE toolkit for spaceflight geometry.
• Intuitive knowledge of VCS systems ﴾Git/GitHub/Forgejo﴿.

Topics: Magnetism, Enceladus, Plasma, MHD
Tutor: Giuliano Vinci
Uploaded: 22/09/2026

Enceladus vents water vapour and ice grains continuously from four fractures of its south polar terrain, the tiger stripes, forming a plume that extends for thousands of kilometres. A spacecraft crossing it traverses a rarefied but non‐negligible medium, and is subject to an aerodynamic resistance force which perturbs its trajectory and loads its attitude control system. Cassini crossed the plume repeatedly between 2005 and 2015, and the effect was large enough to be reconstructed from attitude control telemetry.

The magnitude of this force is governed almost entirely by the mass density encountered along the trajectory, which therefore has to be modeled. The published models range from empirical fits calibrated on attitude control data to free‐molecular and Monte Carlo treatments of the individual vents, differing by orders of magnitude in cost and in the altitude range over which they hold. A previous bachelor’s thesis in this laboratory implemented two empirical models under simplifying assumptions. The candidate will select a density model according to explicit criteria, implement it in Python over SPICE flyby geometry, and reduce
the result to a parametrised resistance law, with an associated uncertainty, applicable to any trajectory crossing the plume.

Expected Outcomes:
• A critical review of the published plume density models, justifying the one adopted.
• A Python implementation of the model over the SPICE geometry of the Cassini flybys.
• A parametrised resistance law with an uncertainty, validated on the Cassini flybys.

Requirements:
• Preliminary knowledge of Python is appreciated, but may be acquired during the thesis.
• Basic fluid dynamics and orbital mechanics, plus solid English reading skills.

Acquired skills:
• Scientific programming in Python, over the NASA/NAIF SPICE toolkit ﴾SpiceyPy﴿.
• Basic rarefied gas dynamics, the regime of spacecraft flight inside planetary exospheres.
• Sensitivity analysis and uncertainty propagation applied to a physical model.
• Git‐based VCS ﴾local and cloud‐based﴿.

Topics: Fluids, Modeling, Missions, Statistics, Enceladus
Tutor: Giuliano Vinci
Uploaded: 22/09/2026