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Reconstructing Galaxy Cluster Mass Maps using Score-based Generative Modeling
- A new approach to reconstruct gas and dark matter maps of galaxy clusters using score-based generative modeling has been developed.
- The model utilizes mock SZ and X-ray images as inputs and generates realizations of gas and dark matter maps based on a learned data posterior.
- Experiments show the model accurately reconstructs radial density profiles in the spatial domain and demonstrates the ability to distinguish between clusters of different mass sizes.
- The diffusion model can be fine-tuned to incorporate additional observables and predict unknown density distributions of galaxy clusters based on real observations.
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