This project focuses on developing a Physics-Informed Neural Network (PINN) framework for analyzing real strong gravitational lensing datasets to study dark matter distribution. Strong gravitational lensing, a key prediction of general relativity, occurs when a massive galaxy or cluster bends light from a background source, creating arcs or Einstein rings. Traditional algorithms struggle or fail entirely when applied to real lensing datasets due to observational complexities and noise. By leveraging PINNs, the project will integrate physical laws directly into the learning process, enhancing the accuracy and interpretability of dark matter inferences. The model will be trained on real lensing images, incorporating observational constraints to refine mass distribution estimates and improve the efficiency of dark matter studies.
Total project length: 175/350 hours.
Intermediate/Advanced
Python, PyTorch, experience with machine learning, knowledge of computer vision techniques, familiarity with autoencoders.
Please use this link to access the test for this project.
Please DO NOT contact mentors directly by email. Instead, please email ml4-sci@cern.ch with Project Title and include your CV and test results. The relevant mentors will then get in touch with you.