Euclid Computer Vision
Gravitational Lens Data Science
The Euclid space telescope from the European Space Agency (ESA) observes large areas of the sky with the goal of mapping the structure of the Universe, such as dark matter and dark energy. Within this data, strong gravitational lenses appear as distortions in galaxy images. Detecting and interpreting these systems is difficult, not only because they are rare, but also because their appearance depends strongly on how the data is processed and represented.
Euclid Computer Vision focuses on understanding these effects. The project explores how astronomical image data, color construction, scaling choices, and deep learning techniques influence both visual inspection and computational analysis. By combining data science, machine learning, image processing, and computer vision techniques, the project studies how different representations of Euclid data change what information becomes visible and usable.
Dive deeper
The project works with publicly released Euclid image data in both the visible and near-infrared spectrum, using their native astronomical data formats. It investigates instrument-specific characteristics, noise and resolution effects, classification using deep neural network applications, and different approaches to combining multiple bands into scientifically meaningful image representations. The analysis is performed using Python-based data science, modern transformer architectures, and supervised and unsupervised learning techniques.