Improved Monitoring of Antarctic Ice Shelves using the Sentinel-1 TOPSAR Acquisition Mode
Quentin Glaude (CSL)

Evaluating the stratospheric circulation and its variability in a Chemistry-Climate Model with reanalyses and observations of nitrous oxide
Daniele Minganti (SPHERES)

https://www.sciences.uliege.be/cms/c_9152696/fr/soutenance-de-these-de-daniele-minganti

Apsidal Motion in O-Star Binaries. Constraining the internal structure of the stars
Sophie Rosu (GAPHE)

Stars more massive than about ten solar masses play a key role in many processes in the Universe (winds, powerful supernova explosions, chemical enrichment of the Universe). More than 70% of them are bound by gravitational attraction to a companion star and, together, they orbit around each other on an elliptic orbit. The tidal interactions occurring between the two stars give rise to the slow precession of the orbit, called the apsidal motion. The rate of this motion is directly related to the internal structure constant of each star, which is a measure of the mass distribution between the core and the external layers of the star. Measuring the apsidal motion rate hence provides a diagnostic of the otherwise difficult to constrain internal structure of stars and offers a test of our understanding of stellar structure and evolution.

Revisiting key observational evidence for dark matter and dark energy
Clémentine Hauret (OrCA)

https://www.sciences.uliege.be/cms/c_9797996/fr/soutenance-de-these-de-clementine-hauret

Characteristics of Jupiter's Polar Auroral Bright Spot based on Juno in situ and Remote Sensing Observations
Kamolporn 'Lin' Haewsantati (LPAP)

This work focuses on the dynamics of an auroral feature in Jupiter’s polar region, which is called Jupiter’s polar auroral bright spot. The bright spot characteristics are studied, based on images taken by the Ultraviolet Spectrograph (Juno-UVS) onboard Juno spacecraft. The results show that the bright spot’s power is in the range of gigawatts. They are found to be in various SIII longitudes, corresponding to various local times. Moreover, the bright spots are generally located in regions where high-energy particles are usually found. The most interesting result is the reappearance of the bright spot, suggesting quasi-periodic behavior. In addition, the particle distributions, the plasma waves, and the magnetic field are observed by in situ instruments onboard the Juno spacecraft on 3 events during which the Juno positions were above the bright spot positions. The results show that the intensification of upward Whistler-mode waves and upward electron enhancement simultaneously occurred, suggesting the wave-particle interactions were taking place. These processes could play a main role in accelerating particles, causing the bright spot emissions. However, other processes causing particle accelerations should be further discussed for their roles in causing the bright spot emission as well.

Advanced Data Processing Techniques for Exoplanet Detection in High Contrast Images
Carl-Henrik Dahlqvist (PSILab)

High contrast imaging (HCI) is one of the most challenging techniques for exoplanet detection, but also one of the most promising. The main difficulties encountered with HCI arise from the small angular separation between the host star and the potential exoplanets, the flux ratio between them, and the image degradation caused by the Earth's atmosphere. Although adaptive optics and coronagraphic techniques strongly improved the image quality and the dynamic range, residual aberrations, called quasi-statics speckles, still limit the achievable contrast. Different post-processing techniques along with observing strategies have been proposed in the last decade to deal with these quasi-static speckles, whose shape and intensity are similar to potential planetary companions. This PhD thesis builds upon these recent advances, focusing mainly on the development of a new data processing technique to unveil fainter planetary signals from angular differential imaging (ADI) sequences, and to retrieve their properties.

A machine learning approach to the search for gravitational waves emitted by light objects
Grégory Baltus (IFPA)

With GW170817, gravitational waves have shown themselves to be very useful for multi-messenger astronomy. Combining the information from multiple channels such as gravitational waves, gamma-rays, neutrinos, etc. can lead to great physics. Contrarily to the electromagnetic telescopes, a gravitational wave interferometer surveys the entire sky. They do not have to focus on a small portion of the celestial sphere as do standard telescopes. It is also known that for binary neutron stars, the electromagnetic counterpart is produced during the last phase of the merger, whereas the gravitational wave signal can be detected several minutes before these last stages. If one is able to detect this signal before the merger and infer the sky location, gravitational wave astronomy can then send an alert and produce a sky map indicating where the astronomer can point their telescopes to see an electromagnetic counterpart.

The standard technique to detect these compact binary coalescences is matched filtering. The principle is to compute a template bank of pre-computed waveforms and match them with the data strain coming from the LIGO and Virgo interferometers. This thesis starts by illustrating a matched filter search with a project to detect long signals coming from sub-solar coalescence.

Recently, some matched filtering pipelines have started to adapt their method to search for gravitational waves with only the early stage of the signal. Other methods are beginning to be developed for this type of research. This thesis presents new methods, based on machine learning, to detect the early phase of a binary neutron star merger. We have developed multiple convolutional neural networks looking directly at the strain data of the detector to detect binary neutron stars before the merger.

The last step to produce an early warning for the astronomer is to create a sky map indicating the location of the event. We therefore shortly discuss how to accomplish this through a machine learning method for the whole signal, and also mention how it can be adapted to the early part of the signal.

Accurate cosmological inference in a gravitationally distorted Universe:
Learning from simulated gravitationally lensed systems
Lyne Van De Vyvere (Orca)

Our Universe as we see it is in fact a deformed image of what it is intrinsically. The light emitted by distant galaxies takes time to reach us. On its way, the light can be deflected by massive galaxies, and hence provides us with a deformed version of the background galaxies. Such phenomenon is called "gravitational lensing". It allows us to better understand the distribution of mass in the Universe and better apprehend the evolution of the latter. Gravitational lensing thus enters the field of cosmology, which aims at studying the Universe as a whole. In my thesis, I provided ways to improve the process of simulating and modeling gravitational lenses. Moreover, I also quantified systematic biases in the inference of the Hubble constant, a key cosmological parameter, when calculated based on lensing systems. Those biases can be substantial for given lensing systems, but a more general analysis considering populations of lenses remains unbiased yet with greater uncertainty.

Progress in hybrid diffractive/refractive lens solutions for compact space IR imager
Victor Laborde (STAR)