Bachelorarbeiten bei Prof. Mertsch in 2023

 

Constraining dark matter with galaxy clusters

Context:
Gravitational effects point to the need for non-baryonic dark matter, yetunravelling its particle nature will requires observing some non-gravitational interactions. In a large class of models, dark matter possesses a tiny, fractional charge, and the resulting two-body self-interactions of dark matter can modify the structure of astrophysical bodies, like clusters of galaxies. However, it has recently been recognised that such tiny charges can also induce plasma-like behaviour that would lead to much stronger constraints than the two-body interactions.

Goals:
You will start by studying instabilities in ordinary plasmas before applying this to dark plasmas. We will then consider ways of how such instabilities would affect the measurable properties of a galaxy cluster, e.g. the spatial distribution of dark matter. Ultimately, we will use this to estimate what fractional charges can be constrained as a function of dark matter mass.

Requirements:
Familiarity with python or Mathematica and an interest to learn about dark matter and galaxy clusters.

Differentiable programming for cosmic ray models

Context:
Galactic cosmic rays are measured with unprecedented accuracy by the most advanced detectors in space. Yet, there are a number of discrepancies between the data and our models. More sophisticated models typically carry a large number of free parameters, thus requiring efficient fitting methods. Luckily differentiable programming, used abundantly in machine learning, provides the necessary tools for the job.

Goals:
We will build a model of galactic cosmic ray propagation and, comparing to existing data, we will constrain the most important model parameters. Differentiable programming will then help us in improving the code. In the end we will compare our results to previous models and quantify the efficiency of our new methods.

Requirements:
Familiarity with python and an interest in understanding up-to-date galactic cosmic ray propagation.

Cosmic-ray induced ionisation and gamma-rays in starburst galaxies

Context:
Some galaxies have startformation rates that are orders of magnitude larger than the Milky Ways. Interestingly, interactions between cosmic rays and dense gas in these so-called starburst galaxies result in large gamma-ray fluxes and thus many starburst galaxies have been observed by current gamma-ray telescopes. Some of these galaxies also have data on cosmic-ray induced ionisation derived from molecular line surveys. The combination of these observations provides a unique opportunity to study starburst galaxies in detail.

Goals:
We will develop models to describe cosmic ray spectra in starburst galaxies. These models will then be confronted with observational data to constrain the most important parameters for cosmic-ray transport and acceleration in starburst galaxies.

Requirements:
Familiarity with python or C++ and an interest to learn about high-energy astrophysics.

Solving a swiss cheese model of the Galaxy on GPUs

Context:
Recent gamma-ray observations have shown that the Galaxy in which cosmic rays propagate, is much more structured than previously thought. Specifically, the diffusivity can vary by orders of magnitude in small, bubble-shaped regions. Given the small size of these regions, their effect is usually ignored, but we have recently shown that they do affect observable spectra of cosmic rays. This was based on simplifying assumptions, which need to be justified more carefully.

Goals:
You will start by acquainting yourself with stochastic differential equations (SDEs), the mathematical formalism to be applied to the transport in inhomogeneous media. We will write our own code to solve the SDEs in a Monte Carlo fashion, first on CPUs, later on GPUs. Ultimately, we will quantify the statistics of cosmic ray transport and check whether the analytical approximations are appropriate.

Requirements:
Ideally, familiarity with C/C++ as well as a willingness to get acquainted with a different computing paradigm.