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

  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},
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

A Machine-learning Approach to Enhancing eROSITA Observations

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



Adam: A Method for Stochastic Optimization

This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.

CMB-HD: An Ultra-Deep, High-Resolution Millimeter-Wave Survey Over Half the Sky

A millimeter-wave survey over half the sky, that spans frequencies in the range of 30 to 350 GHz, and that is both an order of magnitude deeper and of higher-resolution than currently funded surveysโ€ฆ

Note on evaluation

ApJS, 192, 9 Villaescusa-Navarro F., et al., 2021a, arXiv e-prints, p. arXiv:2109.10915 Villaescusa-Navarro F., et al., 2021b

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Emulating tSZ images

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