Reconstructing Galaxy Cluster Mass Maps using Score-based Generative Modeling
<|reference_start|>Reconstructing Galaxy Cluster Mass Maps using Score-based Generative Modeling: We present a novel approach to reconstruct gas and dark matter projected density maps of galaxy clusters using score-based generative modeling. Our diffusion model takes in mock SZ and X-ray images as conditional observations, and generates realizations of corresponding gas and dark matter maps by sampling from a learned data posterior. We train and validate the performance of our model by using mock data from a hydrodynamical cosmological simulation. The model accurately reconstructs both the mean and spread of the radial density profiles in the spatial domain to within 5\%, indicating that the model is able to distinguish between clusters of different sizes. In the spectral domain, the model achieves close-to-unity values for the bias and cross-correlation coefficients, indicating that the model can accurately probe cluster structures on both large and small scales. Our experiments demonstrate the ability of score models to learn a strong, nonlinear, and unbiased mapping between input observables and fundamental density distributions of galaxy clusters. These diffusion models can be further fine-tuned and generalized to not only take in additional observables as inputs, but also real observations and predict unknown density distributions of galaxy clusters.<|reference_end|>
@article{hsu2024reconstructing,
    title={Reconstructing Galaxy Cluster Mass Maps using Score-based Generative
  Modeling},
    author={Alan Hsu, Matthew Ho, Joyce Lin, Carleen Markey, Michelle Ntampaka, Hy
  Trac, Barnab'as P'oczos},
    journal={arXiv preprint arXiv:2410.02857},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.02857},
    primaryClass={astro-ph.CO cs.LG}
}