Dissertationen

The International Terrestrial Reference Frame (ITRF) is fundamental for precise positioning, navigation, and Earth science applications. The scale is one of the three fundamental parameters that define the ITRF, together with its origin and orientation. Traditionally, scale has been realized using Very Long Baseline Interferometry (VLBI) and Satellite Laser Ranging (SLR). The recent availability of pre-launch satellite antenna calibrations for Galileo and GPS Block IIIA now enables Global Navigation Satellite Systems (GNSS) to contribute directly to the ITRF scale. However, understanding how modeling deficiencies in GNSS orbit determination propagate into the scale, and why differences remain between scale realizations derived from GNSS, VLBI, and SLR, remains an open problem. This research focuses on investigating these effects in GNSS-based solutions to improve the consistency and reliability of the ITRF scale.

Studying distant objects in space can only be performed by analyzing their light in detail, a method called spectroscopy. We will study solar and stellar flares by designing and developing a spectrograph specifically tailored for this task. Every instrument has tradeoffs to reach the respective scientific and technical requirements, therefore a dedicated instrument is required to optimally observe flares. This unique instrument, primarily implemented at Zimmerwald Observatory, will be optimally designed to measure the light's optical spectrum of the Sun and stars and their polarization, giving us further details on solar and stellar flares.

The Earth's gravitational field is not uniform, as it depends on the mass below the surface. Factors such as the presence of ground water or ice loss can be determined using tracking data from Low Earth Orbiting (LEO) satellites. Until now, most gravity field solutions are based on only scientific LEO missions, in particular dedicated gravity missions but also missions serving other purposes, which are expensive to build and take a long time to cover the entirety of Earth, as only one or two satellites are involved. Our research topic is to determine, whether larger constellations of commercially built nanosatellites can be used to produce gravity field solutions of similar quality to those of non-dedicated scientific missions.

The Sun offers a unique opportunity: it provides spatially resolved data - an advantage rarely encountered when studying remote celestial bodies. Of particular importance are "solar flares", due to their effects on Earth’s atmosphere and technology. While prior efforts have made notable progress in understanding individual flares, investigating the statistical characteristics of hundreds of such flares still remains. The integration of Machine Learning (ML) into the field of solar physics presents a unique solution. However, the task is not just to predict when solar flares will happen, but also to understand the physics of solar flares. This renders the application of ML to Solar Physics both challenging and inherently rewarding.

With the number of objects in Earth's orbit increasing rapidly, space debris becomes an ever greater risk for spaceflight. Determining accurate orbits of space debris objects is especially difficult. Space debris laser ranging is a technique measuring the time of flight between laser pulses being emitted from a ground station and being detected after they got reflected from the space debris object at the same, or different stations. In my thesis, I investigate the possible orbit improvements of such multi-static laser ranging, utilizing a new high power laser system at Zimmerwald observatory and computer simulations.

Space weather refers to the dynamic conditions in our Solar System caused by the Sun's activity, capable of creating both stunning and disturbing phenomena in our atmosphere. With ~12,000 satellites orbiting Earth and projections of over 10,000 more by 2030, understanding the impact of space weather is vital for preventing satellite collisions. To unravel when, why and how the Sun affects our spacecraft, we apply data science and machine learning methods to solar observations alongside precise orbits of scientific satellite missions. This effort aims to enhance our understanding of key space weather parameters and their influence on satellite behavior.

The Sun’s activity follows an 11-year cycle, during which periods of increased solar activity are associated with a greater likelihood of solar flares and coronal mass ejections. These solar events can significantly affect Earth’s technological infrastructure, underscoring the importance of comprehending and forecasting the Sun’s activity. Sunspots serve as a primary indicator of solar activity. However, the process governing their evolution remains largely unexplained. We use advanced machine learning models to analyse solar data and predict how active regions evolve over time. The research not only aims to improve our predictive capabilities but also deepens our understanding of the underlying solar dynamics.

The attitude of non-cooperative resident space objects (RSOs) is an important aspect of space situational awareness. Space sustainability efforts, such as active debris removal, depend on information about the tumbling modes and rates of RSOs. Information about the attitude can be extracted from the varying brightness of the object over time, called light curves. In my thesis I investigate the use of single-photon avalanche diodes to obtain light curves with a particularly high temporal resolution. Based on this data I try to obtain attitude information using light curve inversion algorithms and a priori knowledge of the observed object.

The Sun generates the most powerful explosions in the Solar System, known as solar flares. These flares release intense bursts of radiation and propel solar winds toward Earth, posing serious risks to satellites, communication networks, and power grids. Consequently, the scientific community has been diligently working on methods to forecast solar flares. However, due to the Sun’s complex dynamics, these efforts have yet to yield fully reliable results. In my PhD, I aim to advance the field of solar flare forecasting by applying machine learning techniques to enhance the accuracy and precision of predictive models.

My research focuses on observational solar physics, using high-resolution data from the Swedish Solar Telescope to study the structure and dynamics of the solar atmosphere during flares. These energetic events are characterized by rapid and complex changes in magnetic fields and plasma dynamics. Spectropolarimetric observations allow us to infer key atmospheric parameters and build models of the evolving atmosphere. Our goal with these observations is to understand the conditions before, during, and after flares, as this knowledge is essential for space weather forecasting.

Solar flares release huge amounts of energy and can eject material into space towards Earth. Thereby, the electronics of satellites or airplanes can be disturbed or even large-scale power failures can occur. We analyze observations of the Sun utilizing machine learning to build prediction models and find the configurations of the solar atmosphere that lead to an eruption. The Sun is still not well understood, learning more about it is of both scientific importance and practical value to better protect our infrastructure against threats from space.

Positioning systems, such as GPS or Galileo, play a vital role in geodetic sciences, since their observations are routinely used to define the frame upon which global scale Earth observing missions are referred to. Additionally, the proper physical description of the involved satellite orbits allows us to indirectly determine geophysical properties of the Earth (for example, its orientation at a microarcsecond level). This is only possible due to the ongoing effort that we dedicate to the development of novel processing algorithms and cutting-edge physical models.

Probes exploring the solar system are regularly communicating with the Earth. Their trajectory can be reconstructed thanks to, e.g., the Doppler effect (frequency shift). This PhD thesis focuses on Europa and Callisto, two of Jupiter's moons. Considering probes in orbit around them, we estimate their geodetic parameters, such as their gravity fields, from the perturbations they induce on the probe's orbit. Knowing more about these parameters can help us better constrain the internal structure models of Europa and Callisto, such as the density and the composition of their internal layers e.g., the existence of a subsurface liquid ocean on Europa.

The standard procedure is to jointly process GNSS satellite and ground station data to determine the orbits of the GNSS satellites and the precise coordinates of the ground stations, such as the one in Zimmerwald. The orbits of low Earth orbiting satellites (LEOs) are computed separately, assuming that the GNSS satellites' trajectories are correct. In my work, however, all three elements are jointly processed, and additional geodetic parameters, such as the Earth’s center of mass, are calculated. The goal of my work is to determine whether, and to what extent, the derived parameters improve when all data are processed together. Cyril's thesis

Satellite Laser Ranging (SLR) is a geodetic technique, which measures the time-of-flight of ultra-short laser pulses emitted from a ground station to an Earth orbiting satellite and reflected back to the ground station. Ideally, the satellites are spherical, small, heavy, and equipped with reflectors. Based on investigating these measurements, one can draw conclusions about changes in the system Earth, e.g., in the lengths of a day, the position of the Earth's axis of rotation or mass re-distributions. Linda's thesis

In my thesis, I analysed specific problems involving the enhancement of optical observation techniques to pave the way towards a sustainable use of the outer space. One challenge comprised the development of a new method for being able to use the laser ranging observation technique with inaccurate ephemerides or even after the discovery of a new resident space object. Other challenges comprised the observation during daylight, and the fusion of observables to gain more information about the dynamics of the observed targets. Julian's thesis

A key to understanding the dynamic system Earth is the continuous observation of its time-variable gravity field, which allows for conclusions about mass movements, typically water transport on and near the Earth's surface. As a consequence, these measurements map climate change. The gravity field is monitored by dedicated satellite missions, e.g., the Gravity Recovery And Climate Experiment (GRACE) and its successor GRACE Follow-on, which ultra precisely measure distance changes between a pair of Earth orbiting satellites separated by a few hundred kilometres. These observations lay the foundation for modelling of the Earth’s time-variable gravity field, typically on a basis of monthly snapshots. Image credit: NASA. Martin's thesis