# Investigating cosmological GAN emulators using latent space interpolation

@article{Tamoinas2021InvestigatingCG, title={Investigating cosmological GAN emulators using latent space interpolation}, author={Andrius Tamo{\vs}iūnas and Hans A. Winther and Kazuya Koyama and David Bacon and Robert C. Nichol and Ben Mawdsley}, journal={Monthly Notices of the Royal Astronomical Society}, year={2021} }

Generative adversarial networks (GANs) have been recently applied as a novel emulation technique for large-scale structure simulations. Recent results show that GANs can be used as a fast and efficient emulator for producing novel weak lensing convergence maps as well as cosmic web data in 2D and 3D. However, like any algorithm, the GAN approach comes with a set of limitations, such as an unstable training procedure, inherent randomness of the produced outputs, and difficulties when training…

## 6 Citations

### Emulation of Cosmological Mass Maps with Conditional Generative Adversarial Networks

- PhysicsFrontiers in Artificial Intelligence
- 2021

A novel conditional GAN model is proposed that is able to generate mass maps for any pair of matter density Ω m and matter clustering strength σ 8, parameters which have the largest impact on the evolution of structures in the Universe, for a given source galaxy redshift distribution n(z).

### Can denoising diffusion probabilistic models generate realistic astrophysical fields?

- Physics, Computer ScienceArXiv
- 2022

This work investigates the ability of score-based generative models to generate dark matter mass density from cosmological simulations and images of interstellar dust in two astrophysical contexts, and demon-strate a proof-of-concept application of the model trained on dust in denoising dust images.

### Emulating cosmological multifields with generative adversarial networks

- Physics
- 2022

We explore the possibility of using deep learning to generate multiﬁeld images from state-of-the-art hydrodynamic simulations of the CAMELS project. We use a generative adversarial network to…

### Large-scale dark matter simulations

- PhysicsLiving Reviews in Computational Astrophysics
- 2022

We review the field of collisionless numerical simulations for the large-scale structure of the Universe. We start by providing the main set of equations solved by these simulations and their…

### The CAMELS Multifield Data Set: Learning the Universe’s Fundamental Parameters with Artificial Intelligence

- Computer Science, PhysicsThe Astrophysical Journal Supplement Series
- 2022

A collection of hundreds of thousands of 2D maps and 3D grids containing many different properties of cosmic gas, dark matter, and stars from more than 2000 distinct simulated universes at several cosmic times, designed to train machine-learning models.

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