This project aims to harness the potential of Physics Informed Neural Networks (PINNs) in the study of dark matter, utilizing strong gravitational lensing images. Strong gravitational lensing, a phenomenon predicted by Einstein’s theory of general relativity, occurs when a massive galaxy or cluster of galaxies bends the light from a more distant galaxy into a ring or arc. This effect not only magnifies the background galaxy but also distorts its image, providing a unique opportunity to study the mass distribution of dark matter in the lensing galaxy or cluster.
The project will focus on developing a PINN framework tailored to infer the properties and distribution of dark matter in lensing galaxies. The PINNs will be trained on simulated and observed strong gravitational lensing images, incorporating physical laws governing lensing phenomena directly into the learning process. This approach aims to improve the accuracy and efficiency of dark matter studies by seamlessly integrating physical constraints with deep learning methodologies.
Total project length: 175/350 hours.
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