Strong gravitational lensing is a promising probe of the substructure of dark matter to better understand its underlying nature. Deep learning methods have the potential to accurately identify images containing substructure, and differentiate WIMP particle dark matter from other well-motivated models, including axions and axion-like particles, warm dark matter etc. Gravitational lensing data is often collected at low resolution due to the limitations of the instruments or observing conditions. Image super-resolution techniques can be used to enhance the resolution of these images with machine learning, allowing for more precise measurements of the lensing effects and a better understanding of the distribution of matter in the lensing system. This can improve our understanding of the mass distribution of the lensing galaxy and its environment, as well as the properties of the background source being lensed.
This project will focus on the development of deep learning-based image super-resolution techniques such as conditional diffusion models to enhance the resolution of gravitational lensing data. Furthermore, we will also investigate leveraging the super-resolution models for other strong lensing tasks such as regression and lens finding.
Total project length: 175/350 hours.
Python, PyTorch and relevant past experience in Machine Learning.
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