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 vortex substructure of dark matter condensates and superfluids.
This project will focus on further development of the simulations used in the DeepLense pipeline which utilize the package lenstronomy. This will include working to increase the fidelity of the simulations and also working to expand the range of dark matter models considered for simulation. The project will also include helping to improve and facilitate the creation of data for applications with various ML approaches in this project.
Total project length: 175/350 hours.
Update and streamline code used by project and generate several simulated lensing data sets.
Python and some previous experience with physics or astronomy.
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