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. This project proposes the development of physics-informed diffusion and flow matching models for simulating strong gravitational lensing images. Gravitational lensing is governed by well-defined physical laws and symmetries. We aim to explore strategies for encoding these physical structures directly into generative models so that outputs are not merely statistically realistic but physically consistent. Possible directions include incorporating symmetry-aware architectures, parameterizing the generation process through physically meaningful intermediate representations (e.g., convergence and shear maps), enforcing governing equations as soft or hard constraints, or other approaches the contributor may propose. The resulting model is intended to produce high-fidelity, physics-compliant lensing simulations to augment training data for substructure detection and dark matter classification, while offering stronger generalization and a narrower sim-to-real gap compared to purely data-driven generative approaches.
Total project length: 175/350 hours.
Intermediate/Advanced
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