Master’s Projects

List of proposed projects

Proposed by: Ashutosh Mishra (PhD) & Emma Tolley

Cosmology with Cold DM has been extensively studied in simulations, but observational predictions for alternate models of DM have not been explored as extensively. This is especially true for bosonic or Fruzzy Dark Matter (FDM), where nonlinear interference effects can leave a distinct signature in the matter power spectrum and small scale structure. Unfortunately, the numerical resolution requirements to faithfully follow the FDM dynamics are much harder to fulfil than for the N-body techniques applicable in the CDM case. A new class of neural networks use physics-based constraints to solve the data limitations and generalization problems of traditional neural networks, called Physics-Informed Neural Networks (PINNs). Such neural networks can be constrained to respect any symmetry, invariance, or conservation principles originating from the physical laws that govern the observed data, as modeled by general time-dependent and non-linear partial differential equations. Embedding this prior knowledge allows scientists to develop higher fidelity networks which require much less training data.

FDM obeys the Shroedinger-Poisson equations, which can be embedded in a PINN framework to create fast simulations of cosmological FDM. We are interested in exploring applications of this technique with Quantum neural networks following the framework developed in [1].

[1] https://arxiv.org/pdf/2209.14754
 

Proposed by: David Harvey (Faculty), Yves Revaz (Faculty)

Simulating the formation of galaxies is vital to our understanding how the Universe formed and the underlying physics behind it. By simulating galaxies we can test theories, and probe the nature of the elusive dark matter. However, simulating galaxies can take a long time, in particular, owing to the treatement of the cooling due to  molecules and metals. Indeed, an accurate treatement requires to solve many non-linear inter-dependent equations. If we can speed up the estimation of metal-cooling in galaxies we will be able to dramatically speed up our simulations. In a complete new and unique way, this masters project will aim to use deep learning to quickly and precisely estimate the abundances and cooling of a set of atomic species at play during the formation of galaxies. By constructing a model that can bypass the need to carry out complicated equations we can hopefully speed up the simulations and hence open up the possibility to probe new models of dark matter. The student will get then hands on simulations, machine learning, deep learning and gain experience in using python packages such as tensor flow and the GRACKLE libraries.

Proposed by: Yves Revaz (Faculty), Pascale Jablonka (Faculty)
 
Type of project: Master project/Semester project
 
What is the mass range of the first stars? What are the conditions for them to become black holes or explode at the end of their evolution? What are the chemical signatures expected in either case? Can they be observed and if yes, where should we look ? These are some of  the questions that this project will address. 
To meet this challenge, the work will be based on numerical simulations that reproduce the properties of low mass galaxies. Because they have had very short star formation histories, they are indeed the systems with the highest probability of retaining the signatures of the early supernova explosions. Different models of first stars will be considered, mixing phenomena in the galaxy interstellar medium will be traced and quantified. The results will be used to plan new observations on strategic chemical elements.

 
 
References:
Proposed by: Yves Revaz (Faculty), Pascale Jablonka (Faculty)
 
Type of project: Master project/Semester project
 
Discovered only about fifteen years ago, ultra-faint dwarf galaxies (UFDs) are the faintest galaxies known in the Universe. They are also the most dark-matter dominated ones. As such, they are fundamental probes of the nature of the dark matter. While about fifty UFDs are now identified orbiting the Milky Way, their formation and evolution are still poorly understood. Moreover, recent deep spectroscopic observations have revealed signs of tidal features as a consequence of their interaction with the Milky Way. In these conditions, the classical method used to calculate their dark matter content, so far based on the Virial theorem,  is not valid anymore. Proper N-body simulations must be run.
 
In order to address this challenge, models of UFDs galaxies will be first extracted from cosmological simulations. Their evolution will then be followed  in a realistic Milky Way potential. The impact of the gravitational interaction (disruption, elongation, increase of the velocity dispersion) on both the galaxy stellar content and the dark halo will be systematically studied and compared with the observations.
 
 
 
References:
Proposed by: Yves Revaz (Faculty)
 
Type of project: Master project/Semester project
 
Massive stars are known to end their life as a powerful explosion named core-collapse supernova (CCSN). These explosions not only release a huge quantity of energy, but also release new synthesised chemical elements like carbon, nitrogen, oxygen or iron. By studying the atmosphere of stars. Crucial information on the properties of CCSNe can thus be inferred by studying the composition of stars formed from gas polluted by those supernovae.
 
While stars more massive than about height solar masses where believed to explode as a supernovae,  new stellar models predict that they can also directly collapse to a black hole without releasing any synthetised products. Understanding under which condition a supernova explode is a pressing question in astrophysics.
 
We propose to tackle this problem by studying how massive stars, exploding or not, will pollute the smallest galaxies, the so-called dwarf galaxies. By simulating the evolution of those dwarfs in a cosmological context, including the explosion of massive stars, we will predict the composition of new generations of stars and directly compare them with observed data. Combining numerical predictions with observations will provide new constraints on the properties of supernovae progenitors.
 
 
References:
Proposed by: Yves Revaz (Faculty)
 
Type of project: Master project/Semester project
 
Dwarf galaxies are the faintest galaxies known, however they are by number the most abundant ones. Discrepances between numerical predictions and observations, in particular regarding the size or metal content of the faintest dwarf challenge the current cosmological model.
 
In this work, we propose to improve our model of dwarf galaxies by implementing and testing a new star formation scheme that allows to form and explode individual stars in our galaxy formation code. In this so-called sink particle scheme, sink particles are created from dense and cold gas particles and further generate individual stars.
 
After some update of the code, the student will have first to start by running idealized simulations of inter stellar medium. The method will be further applied to simulate the formation of dwarf galaxy in an evolving portion of the universe.
 
 
 
References:

The VELOCE (VELOcities of CEpheids) project provides unprecedented time-series radial velocity data of 256 classical Cepheids, including 75 spectroscopic binaries. Modeling these data is challenging because several signals are present at once: large amplitude pulsations (speeds of 10-70 km/s over weeks), orbital motion (up to 25 km/s over years), and other modulations, such as multi-periodicity, fluctuating periods, time-variable amplitudes, etc.

The goal of this project will be to extend an existing Markov Chain Monte Carlo code that models orbital motion to include the signals due to pulsational variability. The project can be easily extended to treat fluctuating periods or amplitudes as well. The challenge will be to find efficient implementations that allow to infer a maximum of information from the VELOCE radial velocity curves without overfitting.

In this project, you will learn to

  • work with optical spectra and high-precision radial velocity time series of pulsating stars
  • develop MCMC analysis tools and sharpen your statistics skills
  • contribute to a large Python code base (> 10000 lines) and an ongoing research program

… and more!

This project can be done as a TP-IVb, other 8 ECTS, or Master project.

Contact: Giordano Viviani, Richard Anderson

The VELOCE (VELOcities of CEpheids) project provides unprecedented time-series radial velocity data of 256 classical Cepheids, including 75 spectroscopic binaries. Modeling these data is challenging because several signals are present at once: large amplitude pulsations (speeds of 10-70 km/s over weeks), orbital motion (up to 25 km/s over years), and other modulations, such as multi-periodicity, fluctuating periods, time-variable amplitudes, etc.

The goal of this project will be to develop RV curve fitting methods using Regularization techniques to minimize the number of fit parameters used for representing pulsational variability. Regularization will improve the representation of Cepheid RV curves and allow to obtain more accurate orbital parameters, while also allowing the definition of RV template curves applicable to large spectroscopic surveys.

In this project, you will learn to

  • work with high-precision radial velocity time series of pulsating stars
  • develop regularization techniques for variability analyses and sharpen your statistics skills
  • contribute to a large Python code base (> 10000 lines) and an ongoing research program

… and more!

This project can be done as a TP-IVb, other 8 ECTS, or Master project.

Contact: Giordano Viviani, Richard Anderson

Cepheids are pulsating stars whose radius and brightness vary within a stable period. This feature is particularly important in astrometry since it allows us to measure their distance accurately. And consequently, use them as standard candles to calibrate the cosmic distance ladder. However, many other effects other than their pulsation can modify the incoming signals from these stars, such as the presence of an orbiting star. In these cases, the spectra, and therefore the measured radial velocity, of the Cepheid will contain information from both phenomena that can be complicated to distinguish.

 The aim of this project is to explore a newly developed methodology that could allow us to determine the pulsation and orbit periods of binary Cepheids without using any prior knowledge. This method constructs periodograms calculated using the concept of partial distance correlation, which allows us to effectively distinguish the Doppler shifts due to orbital motion and the spectral line variability induced by the stellar activity.

In this project, the student will work with part of the python package SPARTA and apply it to real study cases. The student will study the limitations and strong points of this method. Understand the precision and accuracy of the results. Propose modifications or improvements to the technique and experiment with them.

Links:

Method: https://ui.adsabs.harvard.edu/abs/2022A%26A…659A.189B/abstract

SPARTA: https://github.com/SPARTA-dev/SPARTA

This project can be done as a TP-IVb, other 8 ECTS, or Master project.

Contact: Giordano Viviani, Richard Anderson

Pulsating stars are extremely useful tools for astrophysics, since their light variations allow to measure distances and probe their interior structure. The current era of large time-domain surveys is revolutionizing our knowledge of pulsations and increases enormously their applicability for distance measurements. This allows us to unravel the structure of the Milky Way, the nearby Universe, and calibrate measurements of the expansion of the Universe.

In this project, we will use the all-sky survey TESS combined with the ESA space mission Gaia to obtain an unprecedented view of low-amplitude multi-periodic long-period variable stars in the Milky Way. The goal of the project will be to identify the variable stars through their variability, determine the variability properties, and calibrate the period-luminosity relations that allow to use them for distance measurements.

In this project, you will learn to

  • access large astronomical data sets through online archives, such as the Gaia archive and MAST
  • process large amounts of photometric time series using python
  • use astrometric (positions, parallax, proper motion) and photometric data to maximum advantage
  • distinguish different variability classes
  • calibrate period-luminosity relations

… and more!

This project can be done as a TP-IVb, other 8 ECTS, or Master project.

Contact: Bastian Lengen, Richard Anderson

Cherenkov Telescope Array Observatory is a major next-generation gamma-ray telescope that is in construction now. The first Large Size Telescope (LST) of CTAO is already operational and takes data, detecting gamma-rays through the Cherenkov glow of high-energy particle showers initiated by gamma-rays penetrating the Earth atmosphere. 

The project is aimed at understanding the methods of gamma-ray data taking and data analysis with CTAO, with a focus on the study of violently variable gamma-ray emission from “blazars”, a special type of Active Galactic Nuclei with supermassive black holes ejecting high-energy particle jets aligned along the line of sight. 

This project can be done as a TP-IVb, other 8 ECTS, or Master project.

Contact: Andrii Neronov, Volodymyr Savchenko