Emulating Sunyaev-Zeldovich Images of Galaxy Clusters using Auto-Encoders

@inproceedings{Rothschild2021EmulatingSI,
  title={Emulating Sunyaev-Zeldovich Images of Galaxy Clusters using Auto-Encoders},
  author={T C Rothschild and Daisuke Nagai and Han Aung and Sheridan B Green and Michelle Ntampaka and John A. Zuhone},
  year={2021}
}
We develop amachine learning algorithm that generates high-resolution thermal Sunyaev-Zeldovich (SZ) maps of novel galaxy clusters given only halo mass and mass accretion rate. The algorithm uses a conditional variational autoencoder (CVAE) in the form of a convolutional neural network and is trained with SZ maps generated from the IllustrisTNG simulation. Our method can reproduce many of the details of galaxy clusters that analytical models usually lack, such as internal structure andโ€ฆย 

Predicting the thermal Sunyaevโ€“Zelโ€™dovich field using modular and equivariant set-based neural networks

Theoretical uncertainty limits our ability to extract cosmological information from baryonic fields such as the thermal Sunyaevโ€“Zelโ€™dovich (tSZ) effect. Being sourced by the electron pressure field,โ€ฆ

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The eROSITA X-ray telescope, launched in 2019, is predicted to observe roughly 100,000 galaxy clusters. Follow-up observations of these clusters from Chandra, for example, will be needed to resolveโ€ฆ

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