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Institut d'Astrophysique et
de Géophysique (Bât. B5c)

Quartier Agora
Allée du 6 août, 19C
B-4000 Liège 1 (Sart-Tilman)
Belgique

Tel.: 04.366.9774
Fax: 04.366.9729

Thèses

 

Aujourd'hui :
14h30  
A machine learning approach to the search for gravitational waves emitted by light objects
Grégory BALTUS
13/10/2022 :
16h00  
Accurate cosmological inference in a gravitationally distorted Universe:
Learning from simulated gravitationally lensed systems

Lyne VAN DE VYVERE
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Recherche avancée
Thè:se suivante Vendredi 30 septembre, 14h30 (7ème thèse 2022)
A machine learning approach to the search for gravitational waves emitted by light objects
Grégory BALTUS (IFPA)

Salle A3, Petits Amphithéâtres - Galerie des Arts
Bâtiment B7b, Quartier Agora, Allée du 6 Août, 15, B-4000 Liège 1 (Sart-Tilman)


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.
Thèse précédante Jeudi 13 octobre, 16h00 (8ème thèse 2022) 
Accurate cosmological inference in a gravitationally distorted Universe:
Learning from simulated gravitationally lensed systems

Lyne VAN DE VYVERE (Orca)

Amphithéâtre 02, Institut de Mathématiques
Bâtiment B37, Quartier Polytech 1, Allée de la Découverte, 12, B-4000 Liège 1 (Sart-Tilman)


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.
Université de Liège > Faculté des Sciences > Département d'Astrophysique, Géophysique et Océanographie : CoWebAGO, Juin 2009.