HYPHY: Deep Generative Conditional Posterior Mapping of Hydrodynamical Physics
- Princeton Univ., NJ (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Univ. of California, Berkeley, CA (United States)
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Generating large-volume hydrodynamical simulations for cosmological observables is a computationally demanding task necessary for next-generation observations. In this work, we construct a novel fully convolutional variational autoencoder (VAE) to synthesize hydrodynamic fields conditioned on dark matter fields from N-body simulations. After training the model on a single hydrodynamical simulation, we are able to probabilistically map new dark-matter-only simulations to corresponding full hydrodynamical outputs. By sampling over the latent space of our VAE, we can generate posterior samples and study the variance of the mapping. We find that our reconstructed field provides an accurate representation of the target hydrodynamical fields as well as reasonable variance estimates. This approach has promise for the rapid generation of mocks as well as for implementation in a full inverse model of observed data.
- Research Organization:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF)
- Grant/Contract Number:
- AC02-05CH11231
- OSTI ID:
- 1986013
- Journal Information:
- The Astrophysical Journal, Journal Name: The Astrophysical Journal Journal Issue: 1 Vol. 941; ISSN 0004-637X
- Publisher:
- IOP PublishingCopyright Statement
- Country of Publication:
- United States
- Language:
- English