FlowLensing: Simulating Gravitational Lensing with Flow Matching
This enables scalable dark matter studies, specifically for probing substructure in cosmological surveys, though it is an incremental improvement using a novel method for a known bottleneck.
The paper tackled the bottleneck of slow high-fidelity gravitational lensing simulations by introducing FlowLensing, a flow-matching model that achieves over 200× speedup compared to classical simulators while maintaining high fidelity.
Gravitational lensing is one of the most powerful probes of dark matter, yet creating high-fidelity lensed images at scale remains a bottleneck. Existing tools rely on ray-tracing or forward-modeling pipelines that, while precise, are prohibitively slow. We introduce FlowLensing, a Diffusion Transformer-based compact and efficient flow-matching model for strong gravitational lensing simulation. FlowLensing operates in both discrete and continuous regimes, handling classes such as different dark matter models as well as continuous model parameters ensuring physical consistency. By enabling scalable simulations, our model can advance dark matter studies, specifically for probing dark matter substructure in cosmological surveys. We find that our model achieves a speedup of over 200$\times$ compared to classical simulators for intensive dark matter models, with high fidelity and low inference latency. FlowLensing enables rapid, scalable, and physically consistent image synthesis, offering a practical alternative to traditional forward-modeling pipelines.