GRAPPA MSc Thesis Projects

 

The following projects are Master Thesis Projects that have been defined by GRAPPA faculty members. Other thesis projects may also be available, please contact the relevant faculty members. See also GRAPPA members’ research interests. Please note that this is an evolving list, so come back in the future.

See the list of completed thesis projects.

Projects with a September 2020 (or later) start:

Implementing cosmic ray acceleration in (otherwise ideal) general relativistic magnetohydrodynamics (GRMHD) simulations

The current state of the art in modelling black hole accretion is to set up a space-time grid around a black hole, assuming some underlying metric such as Kerr, seed it with a rotating torus of magnetised plasma, and solve the equations of idealised MHD and mass/momentum conservation per time step on the grid. Using this method we can study how the matter falls into the black hole, how turbulence and instabilities form and how magnetic fields get amplified, and/or reconnect, and help channel energy into powerful outflows called jets that can extend well beyond the host galaxy. In my group we use GRMHD simulations to model the Event Horizon Telescope (EHT) images of black holes, as well as try to understand the formation and properties of the jets, which we also think are the sources of ultrahigh energy cosmic rays (UHECRs) and neutrinos. But these simulations have limitations due to computational expense, and one key missing piece is the acceleration of particles, which is a dissipative (and thus non-ideal) process.

We are looking for a student who is motivated to work on a more computationally intense project, to explore and implement new particle acceleration modes within our group-developed, GPU-accelerated GRMHD code HAMR. There are two directions we are considering: 1) collaborating with the group of Damiano Caprioli to study ESPRESSO acceleration using our high-resolution 3D simulations, and/or 2) developing new modules within our code to track and energise high energy protons. The student will be embedded in a fun and supportive group, and be cosupervised by former students who helped develop the code. This is an extremely active topic at the moment and will bring the student into contact with several international researchers in the wider team, as well as provide in depth knowledge on black holes and computational astrophysics that are in high demand on the PhD market.

Literature:

  • HAMR: https://arxiv.org/pdf/1912.10192, https://arxiv.org/pdf/1904.03243.pdf,
  • ESPRESSO: https://inspirehep.net/literature/1754264

Contact: Sera Markoff

 

Confronting self-interacting dark matter with observational data

Dwarf galaxies are dark matter (DM) dominated systems, and therefore provide ideal laboratories to study not only the DM halo and galaxy structure, but also the nature of DM. It has been proposed that DM particles experience collisions against each other, and to further constrain the rate of these collisions, recent studies (e.g. Valli & Yu 2018) have derived a semi-analytic approach for describing self-interacting DM halo profiles, called the Jeans model. This approach is only valid in the regime where the mean free path is larger than the typical size of the collisional region (corresponding to a small collision cross section). A recent study (Correa 2020), however, is predicting that the dwarf galaxies that orbit around the Milky Way are in gravothermal core-collapse, a state where DM-DM collisions increase the density in the central regions. This scenario invalidates the Jeans model analysis of these systems, as well as the current upper limits of the DM collision cross sections.

The goals of this research project are to derive an alternative model for describing self-interacting DM halo profiles under gravothermal collapse, use the latest observational data from the local dwarfs to constrain their density profile and provide new upper limits to the DM collision cross sections. During the first part of the project the student will study the density profile of self-interacting DM haloes using cosmological simulations of galaxy formation.

By carrying out this project, the student will learn about the observations of galaxies from the local group, cosmological simulations of galaxy formation, as well as the theory of structure formation.

References
Valli & Yu (2018) https://arxiv.org/abs/1711.03502
Correa (2020) https://arxiv.org/abs/2007.02958

Contact: Camila Correa and Shin’ichiro Ando

 

Galactic clues to the nature of dark matter

Understanding the nature of dark matter (DM) has become one of the most pressing questions in modern physics and cosmology. Evidence for its existence is exclusively based on its gravitational interactions, as we still know very little about its particle properties. Recent studies are investigating the possibility that DM particles experience collisions against each other. The rate of such collisions can be constrained from observed galaxy density profiles, spanning from dwarf galaxy scales (e.g. Correa 2020) to cluster scales (e.g. Sagunski et al. 2020). However, a theoretical model for the DM particle interactions, that connects the recent observational estimations at various galaxy scales, is still missing. The goals of this research project are to derive such model arguing that DM exists in a ‘hidden sector’, where forces between DM particles are mediated by analogues to electroweak or strong forces. The following steps will be to implement the model in a numerical simulation of galaxy formation, produce simulation runs using the supercomputer cartesius, and investigate observational signatures of self-interacting DM.

Numerical simulations have become an almost indispensable tool in astrophysical research. By carrying out this project, the student will learn about cosmological simulations of galaxy formation, parallelisation and high-performance computing. The student will also connect particle physics modelling with cosmology while building a solid understanding of structure formation.

References
Correa (2020) https://arxiv.org/abs/2007.02958
Sagunksi et al. (2020) https://arxiv.org/abs/2006.12515

Contact: Camila Correa and Shin’ichiro Ando

 

Higher order statistics of the gamma-ray data

In recent years, discussions on statistical properties of the all-sky gamma-ray data have been found very efficient to constrain properties of astrophysical sources and dark matter that can contribute to the gamma-ray background. Thus far, however, people argued only up to a second-order moment, i.e., variance or the angular power spectrum, of the distribution of the photon counts. There is however no need to stop there, and therefore, it is natural to question what extra information the next third-order moment, skewness, would bring on top of what has been achieved. Maybe using the skewness or the angular bi-spectrum will be essential in revealing the nature of particle dark matter still hidden in the existing data. Master student will explore this.

Contact: Shin’ichiro Ando

 

Understanding the high energy emission mechanisms in globular clusters of the Milky Way

With the increased sensitivity of gamma-ray detectors such as Fermi-LAT the number of presently known gamma-ray globular clusters has grown by a factor of ~2 in the last year. The new detections are beginning to provide clues about the origin of the high-energy radiation in the form of emerging patterns and correlations among observed quantities such as gamma-ray luminosity, stellar mass and interstellar radiation energy density. But there are still many questions about the mechanisms of emission and intracluster environmental properties. This project will re-examine these emerging patterns and correlations by carefully studying the population of currently undetected globular clusters.

Contact: Shin’ichiro Ando

 

A realistic assesment of CTA sensitivities to dark matter and millisecond pulsars in the Andromeda Galaxy

Future gamma-ray telescopes such as CTA will allow comparative studies of cosmic rays (CRs) and high-energy objects in the Milky Way (MW) and in other, external galaxies such as Andromeda. Measurements with the Fermi-LAT telescope revealed that the flux from Andromeda is confined to the inner regions of the galaxy and does not fill its galactic disk or extend far from it. The gamma-ray signal is not correlated with regions rich in gas or star-formation activity suggesting that the emission is not interstellar in origin. Alternative and nonexclusive interpretations are that the emission results from a population of millisecond pulsars dispersed in the bulge and disk of Andromeda by disrupted globular clusters or from the decay or annihilation of dark matter particles. This project will estimate the sensitivity of the upcoming CTA gamma-ray telescope to DM annihilation and MSPs at the Andromeda Galaxy. We will introduce a statistical framework for including systematic errors and estimate the consequent degradation in sensitivity. The morphology of the signal at very high energies might allow to distinguish the DM from MSPs hypothesis in the Andromeda galaxy.

Contact: Shin’ichiro Ando

 

Searching for dark matter with gravitational waves

The discovery of gravitational waves has opened new exciting opportunities for fundamental physics. One of the most intriguing aspects of this new “window in the universe” is the possibility to study in unprecedented detail the environment around black holes, and a team of GRAPPA researchers has recently shown that these observations can set extraordinarily stringent constraints on the mysterious dark matter that appears to permeate the universe at all scales. In this project we will explore the interplay between gravitational waves, black holes and dark matter. We will focus in particular on the possibility to probe the fundamental nature of dark matter by looking at how it clusters around black holes, and on its subtle impact on the gravitational waveform produced in the merger of black hole binaries. The project will involve both analytical and numerical work, and will be conducted under supervision of G. Bertone, and in collaboration with other GRAPPA staff and postdocs. 

Contact: Gianfranco Bertone

 

The Hubble Tension

One of the fundamental cosmological parameters is the Hubble constant, which is related to the age of the universe. A way to determine the Hubble constant is to measure the expansion rate of the universe using supernova explosions. Unfortunately, these measurements don’t quite agree with the value of the Hubble constant inferred from the cosmic microwave background. To reconcile these measurements might require a change of the standard model of cosmology. 
 
In this thesis, you will first study the observations that led to the Hubble tension and then explore possible resolutions due to new physics.
 
References: 1907.10625, 1908.03663
 
 
 

The Cosmological Bootstrap

The past decade has seen an explosion of progress in our understanding of scattering amplitudes in gauge theory and gravity. New bootstrap methods have revealed hidden symmetries and new mathematical structures that are completely invisible in the standard approach of Lagrangians and Feynman diagrams. Inspired by these developments, the bootstrap philosophy has recently been applied to cosmology. 
 
In this thesis, you will first study the revolutionary new insights in the field of scattering amplitudes and then explore their connections to cosmology.
 
References: 1811.00024, 1910.14051
 
 

Gravitational lensing image analysis with deep probabilistic programming

Recent breakthroughs in machine learning, in particular deep learning, have a profound impact on how data analysis and modeling in many scientific areas are done. In particular, deep probabilistic programming techniques allow to combine the universal power of deep neural networks with specific scientific predictions derived from first principles.

The nature of dark matter, which constitutes more than 80% of the mass in the Universe, remains unknown. Gravitationally lensed images of distant quasars and galaxies allow us to study the dark matter distribution in the Universe at small scales. This delivers important information about various particle physics models for dark matter.

In this project, the student will first learn advanced deep learning analysis tools (probabilistic programming, geometric deep learning, contrastive learning), as well as the physics of gravitationally lensed images. The student will then explore various ways to improve the sensitivity of current machine-learning based analysis pipelines towards dark matter substructure.

Contact: Christoph Weniger

 

Projects on multimessenger signatures of supernovae and compact object mergers

 
Masters student projects are available in the GRAPPA multimessenger astrophysics group led Samaya Nissanke and Philipp Moesta. Projects can involve modelling compact object mergers and supernovae via
numerical simulations, analyzing and using simulation outputs to build (semi-)analytic models to capture the physics
of these systems, and using simulation outputs and models to connect and compare to available gravitational-wave
and electromagnetic observational data. Projects can develop a large variety of skills and expertise including high-performance computing, data analysis, and machine learning.
 

 

XENON1T Data Analysis

The XENON collaboration has used the XENON1T detector to achieve the world’s most sensitive direct detection dark matter results and is currently building the XENONnT successor experiment. The detectors operate at the Gran Sasso underground laboratory and consist of so-called dual-phase xenon time-projection chambers filled with ultra-pure xenon. Our group has an opening for a motivated MSc student to do analysis with the data from the XENON1T detector. The work will consist of understanding the detector signals and applying machine learning tools such as deep neutral networks to improve the reconstruction performance in our Python-based analysis tool, following the approach described in arXiv:1804.09641. The final goal is to improve the energy and position reconstruction uncertainties for the dark matter search. There will also be opportunity to do data-taking shifts at the Gran Sasso underground laboratory in Italy.

Contact: Patrick Decowski

DARWIN Sensitivity Studies

DARWIN is the “ultimate” direct detection dark matter experiment, with the goal to reach the so-called “neutrino floor”, when neutrinos become a hard-to-reduce background. The large and exquisitely clean xenon mass will allow DARWIN to also be sensitive to other physics signals such as solar neutrinos, double-beta decay from Xe-136, axions and axion-like particles etc. While the experiment will only start in 2025, we are in the midst of optimizing the experiment, which is driven by simulations. We have an opening for a student to work on the GEANT4 Monte Carlo simulations for DARWIN, as part of a simulation team together with the University of Freiburg and Zurich. We are also working on a “fast simulation” that could be included in this framework. It is your opportunity to steer the optimization of a large and unique experiment. This project requires good programming skills (Python and C++) and data analysis/physics interpretation skills.

Contact: Patrick Decowski

 

 


Projects from last year: 

 

Various projects on accretion around compact objects and particle acceleration in jets

My group works on a variety of topics spanning astrophysics to astro-particle physics, mostly focused on accretion and subsequent jet production around compact objects, how and where particles are accelerated within them, and their effect on the environment.   On the modeling side, we are working on new models that include hadronic processes and their secondaries, so can start to compare with purely ‘leptonic’ models using precision multiwavelength datasets, this would involve learning some software and applying the models, or for more ambitious students who want to pursue theory, even developing new modules/models.   For more experimental types I would be interested in collaborating with KM3NeT folk on techniques to optimize transient searches (likely together with Shin’ichiro Ando and Aart Heijboer), or using CTA “data challenge” data to make predictions for CTA for Galactic X-ray binaries and AGN.   Advanced programmers could have the option of working with our GRMHD simulations.   Please get in touch to discuss options if you are interested!

Contact: Sera Markoff

Falsifying cold dark matter with variational autoencoders

The gravitational effects of small dark matter halos can leave specific signatures in the surface brightness profile of strongly lensed high-redshift galaxies. Future observations with for instance the planned ELT will provide extremely sensitive probes towards these signatures, and a non-discovery of small halos would contradict predictions from cold dark matter. One of the challenges of strong lensing analyses is the correct modeling of the complex mass distribution of galaxies. In this project, we will use machine learning techniques to model the galaxy mass distribution, learned directly from N-body simulations. To this end, we will train variational autoencoders, which are state-of-the-art approaches to obtain high-fidelity parametric generative models directly learned from data. The work will be an important extension of ongoing activities and analysis pipelines, which already include generative models for high-redshift source galaxies (directly trained on observed galaxies).

Contact: Christoph Weniger

Radio searches for dark matter axion-photon conversion with the Murchison Widefield array

QCD axions are one of the theoretically best motivated candidates for cold dark matter (they are produced as by-product of dynamical solutions to the strong CP problem). Dark matter axions would convert into photons in the strong magnetic field of neutron stars, leading to sharp radio line signals that can be observable with existing radio telescopes. In this project, the student will contribute to the analysis of 4 hours of Galactic center observations obtained from the Murchison Widefield array (MWA). The student can either contribute on the side of data reduction (bringing TBs of data down to radio images with high spectral resolution), or on the side of the modeling of the expected signal from neutron stars in the Galactic bulge. The available data is expected to be strong enough for discoveries, and otherwise would provide the most stringent constraints.

Contact: Christoph Weniger

The artificial physicist

Recent breakthroughs in machine learning, in particular deep learning, have a profound impact on how data analysis and modeling in many scientific areas are done. One exciting development is that sparse modeling of ordinary differential equations and auto differentiation can be used to optimize and learn physics laws (like the Euler equations for fluid dynamics) from raw data. In this project, we will explore how the laws governing various dynamical systems can be learned directly from data. The student will write physics simulators, generate mock data, and write the training pipeline. Several applications are possible:

  • Gravitational wave signals. We would start learning these based on available template waveforms.
  • Gravitational potential. Given the movement of millions of stars (e.g. GAIA data), we will reconstruct the underlying gravitational potential of the Milky Way.
  • Laws of gravity and dark matter structure formation. Using either mock data based on modified gravity, or results from dark matter only N-body simulations, we will infer the laws of (modified) gravity, or effective laws describing the structure grows of dark matter halos in the Universe.

Experience with machine learning and programming is recommended, though not strictly necessary. Other project ideas are welcome as well.

Contact: Christoph Weniger

Searches for X-ray lines from sterile neutrino dark matter

Sterile neutrinos are excellent candidates for particle dark matter in the keV mass range. The decay of these particles could show up as monoenergetic line in X-ray observations. Potential line candidates around 3.5 keV are discussed in the literature since a few years. The student will in this project use machine learning tools to analyse available X-ray data is a much more fine-grained way than previously possible. We will use the full spatial and spectral information available in different data sets, and account for the large number of uncertainties that are associated with the different flux components. Machine learning libraries like pytorch or tensorflow allow here to marginalize over millions of parameters, if necessary. We will use probabilistic programming techniques and variational inference to obtain a proper assessment of the uncertainties involved in the analysis.

Contact: Christoph Weniger

Modeling Lyman alpha spectra and stellar streams with deep probabilistic programming

The temperature of dark matter can be probed with observations of Lyman alpha absorption lines in distant quasars (caused by neutral Hydrogen clouds on the line-of-sight), or by the structure of stellar streams in our Milky Way halo. Usually, the power spectrum is used to analyse variations and structure in these observations. In this project, the student will use the latest developments on the front of deep probabilistic programming (a mixture between deep neural networks and variational Bayesian inference) to learn surrogate models for these observations directly from large computer simulations. These surrogate models will encompass all the relevant correlation structure of the original observations, and we will explore if and how they provide a more sensitive probe of the dark matter temperature than conventionally used approaches.

Contact: Christoph Weniger

Searching for dark matter subhalos using Pulsar Timing Array

Pulsars are the most precise clock. Deviation from regular pulses will indicate gravitational perturbation on their way to telescopes. It can be caused by passage of small dark matter structures, called subhalos. Pulsar Timing Array will have great sensitivity to these subhalos, especially with very small masses. In this project, the student will look into sensitivities of the Pulsar Timing Array in detecting the dark matter subhalos, and question if one can test different dark matter candidates.  There will be other methods to probe dark matter subhalos, which might also be a subject of other thesis projects.

Contact: Shin’ichiro Ando

Diffuse supernova neutrino background

Core-collapse supernova explosions are one of the most spectacular events in astrophysics. They will produce copious amount of thermal (and possibly non-thermal) neutrinos, strong gravitational waves as well as electromagnetic waves. Although the occurrence rate is unfortunately small in the Milky Way, it is possible to detect the supernova neutrinos as diffuse background radiation. The purpose of this project is to provide the most realistic theoretical predictions of the flux of the diffuse supernova neutrino background and discuss the detectability at various detectors around the world. It would reveal the most violent aspect of the Universe.

Contact: Shin’ichiro Ando

Searching for Dark Matter with antiprotons

The first non-gravitational signal from Dark Matter is yet to be unveiled. One of the most promising searching methods is that of indirect detection. This consists in identifying excesses in cosmic ray fluxes (electron/positrons, gamma-rays, neutrinos or antiprotons) which could possibly be produced by Dark Matter emissions. Observation of many different astrophysical environments with high concentration of dark matter has not yet revealed a smoking gun signature for dark matter. One notable exception consists of the recent claim of a tentative detection at the level of 4 \sigma statistical significance of an antiproton excess in AMS-02 data consistent with DM particles of mass ~70-90 GeV self-annihilating into the b\bar{b} channel. This claim, however, needs further scrutiny and in this project we will address this using the most robust data analysis methods and modelling techniques.

Contact: Shin’ichiro Ando

 

Various projects on accretion and jet production around compact objects

My group works on a variety of topics spanning astrophysics to astro-particle physics, mostly focused on accretion and subsequent jet production around compact objects, and their effect on the environment.   “Grappa-esque” projects could involve taking existing semi-analytical models and applying them to new multi-wavelength data sets, or even developing new models (or modules).   For more experimental types I would be interested in collaborating with KM3NeT folk on techniques to optimize transient searches (likely together with Shin’ichiro Ando and Aart Heijboer), or using CTA “data challenge” data to make predictions for CTA using our models.   Advanced programmers could have the option of working with our GRMHD simulations.   Please get in touch to discuss options if you are interested!

Contact: Sera Markoff

What impact do turbulent magnetic fields have on particle acceleration models?

For modeling spectral energy diagrams (SEDs) of supernova remnants one often uses simple models in which gamma-ray and radio and X-ray data are combined, using a single zone and a uniform magnetic field. Energetic electrons produce both gamma-ray emission through inverse Compton scattering and radio to X-ray emission through synchrotron radiation.  Multi-zones are already more advantages, but one aspect is always overlooked  for modeling X-ray synchrotron emission: the fact that it requires turbulent magnetic fields. This means that the constant magnetic field assumption is per definition wrong and that it may have led to biases in maximum energy estimates. For this project the idea is to investigate the impact the assumption of a turbulent magnetic field has on modeling basic particle accelerations models, namely what is the maximum electron energy inside the sources.

Contact: Jacco Vink

Modeling the ionization and heating in the interior of Cassiopeia A

My interests are not only confined to particle acceleration related to supernova remnants, and this project is only remotely related to particle acceleration: We recently measured the internal radio absorption of the remnant Cassiopeia A with LOFAR (Arias et al. 2018, ArXiv:1801.04887). This internal absorption informs us of the unshocked (cold) gas inside the shell, which has not been shocked and inform us about the evolutionary state of the remnant. However, to translate absorption into unshocked mass we need to know the temperature. Based on infrared data the temperature is assumed to be 200 Kelvin, suggesting a large mass of 2 Msun. However, it is not clear how the inside can be this hot, as radiation from the bright shell makes 30 Kelvin more likely. So for this project we would like to model the ionisation and heating in the interior of Cas A. It is likely that the state of the gas is not in equilibrium. So we would like to make time dependent modeling, using simple hydrodynamics and using heating from UV/X-ray emission from the hot shell. This is a challenging, but doable project that will require combining different modeling packages. My PhD student Maria Arias will co-supervise.

Contact: Jacco Vink

Particle acceleration efficiency of solar system shocks

With a past master student, I investigated the particle acceleration efficiency of solar system shocks. The idea was to test whether there was a critical Mach number (v/v_sound= M ~2.2) below which acceleration is suppressed. This project showed that for M > 2.2 the efficiency is not Mach number dependent and is about 5-10%. This project is a follow-up: look for shocks that are below 2.2 and find out whether they can accelerate and what the circumstances are under which they accelerate (since we found a few that indeed do not accelerate). In addition, we want to look at the magnetic field turbulence in the shock regions and connect it to the diffusion processes that are essential for the process of diffusive shock acceleration.

Contact: Jacco Vink

Other Projects

Here is a list of MSc research projects that are available at Nikhef.