OA
Strong gravitational lensing is a direct consequence of general relativity. It refers to the bending of light from a distant source by a massive foreground galaxy, known as the lens. The lensed image of the background source, typically a young star-forming galaxy, appears as a ring or arcs around the lens. Such systems provide a powerful probe of the dark matter content of the lens galaxy. However, strong lenses are rare, so finding them requires surveying vast areas of the sky at high resolution. With its sharp imaging and wide field of view, the Euclid space telescope is expected to find more than 100,000 lenses among roughly 1.5 billion galaxies. Euclid's strong lensing team currently searches for them with computationally expensive deep-learning classifiers. In this thesis, we develop a lightweight alternative based on Zernike polynomials, a complete set of complex orthogonal polynomials on the unit disk, indexed by an order p and a repetition q. We show that the Einstein radius of a lens is inversely proportional to the order, p, of the most strongly excited mode. We decompose 6" x 6" cutouts of Euclid strong lenses and non-lenses into Zernike modes with 0 ≤ p ≤ 40 and q ∈{ 0, 1}, and use the moment amplitudes as features for simple machine-learning methods. Among these, a support vector machine (SVM) with a radial basis function kernel achieves the highest area under the ROC curve (AUC), while positive-unlabelled (PU) learning achieves the highest recall. Neural networks trained with a recall-weighted loss consistently outperform these classical methods. We combine SVM, PU learning, EasyEnsemble and neural networks into an ensemble classifier that reaches an AUC of 0.87 and a recall of 0.77 while requiring about 7000 times fewer operations than the classifiers currently used in the Euclid strong lensing discovery engine. Over the full survey, this would substantially reduce computing time, cost and energy consumption. This work is a step towards efficient and sustainable alternatives to the heavy algorithms used in large astronomical surveys.
- Authors
- Hrishabh Srivastava
- Journal / venue
- cIRcle (University of British Columbia)
- Publication type
- dissertation
- Status