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 DeepLense pipeline that combines state-of-the art of deep learning models with strong lensing simulations based on lenstronomy. The focus of this project is detecting strong lenses from data sets of mock surveys. These new found lenses can then be passed on to the rest of the DeepLense pipeline for detection, classification, and interpretation of dark matter substructure.
Total project length: 175/350 hours.
Python, PyTorch and relevant past experience in Machine Learning.
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