Faculté des Sciences
Faculté des Sciences

Innovative techniques to find strongly lensed systems

Laisney, Clément ULiège
Promotor(s) : Sluse, Dominique ULiège ; Delchambre, Ludovic ULiège
Date of defense : 29-Jun-2023/30-Jun-2023 • Permalink :
Title : Innovative techniques to find strongly lensed systems
Translated title : [fr] Techniques innovantes pour trouver des systèmes fortement lentillé
Author : Laisney, Clément ULiège
Date of defense  : 29-Jun-2023/30-Jun-2023
Advisor(s) : Sluse, Dominique ULiège
Delchambre, Ludovic ULiège
Committee's member(s) : Christiaens, Valentin ULiège
Fays, Maxime ULiège
Language : English
Number of pages : 83
Keywords : [fr] lentilles gravitationnelles
[fr] machine learning
[fr] galaxie
[fr] apprentissage supervisé
[en] gravitational lenses
[en] machine learning
[en] galaxies
[en] supervised learning
[en] SVM
[en] random forest
[en] Multi-Layer Perceptron
Discipline(s) : Physical, chemical, mathematical & earth Sciences > Space science, astronomy & astrophysics
Target public : Researchers
Professionals of domain
General public
Institution(s) : Université de Liège, Liège, Belgique
Degree: Master en sciences spatiales, à finalité approfondie
Faculty: Master thesis of the Faculté des Sciences


[fr] A galaxy-galaxy lens is a phenomenon in which the light of a background distant galaxy is deflected in the vicinity of a massive foreground galaxy. The occurrence of this phenomenon is very rare. The advent of big-data surveys is an opportunity to detect gravitational lenses only if the proper tools are built. This work aims to build such a tool by testing a set of innovative techniques using parametric and non-parametric models to identify the presence of lensed galaxies in a dataset. The dataset used in this work is a simulated dataset combining true galaxy images with lens simulations also based on true galaxies. Based on this simulated dataset, we use simple machine learning algorithms like Support Vector Machine (SVM), Random Forest (RF), or Multi-Layer Perceptron (MLP). This simple method is an asset for the comprehension of the classification process compared to Convolutional Neural Networks (CNN) that are commonly used. In this exploratory work, we found that 91.8\% of the simulated lenses are reported and classified with a precision of 95.6\%. This work reports thus promising results using MLP that are lower but rather close to the performances of a CNN. This work can be improved to reach comparable or even better results than today's state-of-the-art algorithms by studying residual image resulting from the substraction of a light profile to the original image.



Access Master_Thesis_Laisney_Clement.pdf
Size: 3.78 MB
Format: Adobe PDF


  • Laisney, Clément ULiège Université de Liège > Master sc. spatiales, à fin.


Committee's member(s)

  • Christiaens, Valentin ULiège Université de Liège - ULiège > Département d'astrophys., géophysique et océanographie (AGO) > PSILab
    ORBi View his publications on ORBi
  • Fays, Maxime ULiège Université de Liège - ULiège > Département d'astrophys., géophysique et océanographie (AGO) > Ondes gravitationnelles
    ORBi View his publications on ORBi
  • Total number of views 48
  • Total number of downloads 56

All documents available on MatheO are protected by copyright and subject to the usual rules for fair use.
The University of Liège does not guarantee the scientific quality of these students' works or the accuracy of all the information they contain.