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Found 8 papers, 2 papers with code
Probabilistic reconstruction of Dark Matter fields from biased tracers using diffusion models
Galaxies are biased tracers of the underlying cosmic web, which is dominated by dark matter components that cannot be directly observed.
Field-level simulation-based inference with galaxy catalogs: the impact of systematic effects
In particular, de Santi et al. (2023) developed models that could accurately infer the value of $\Omega_{\rm m}$ from catalogs that only contain the positions and radial velocities of galaxies that are robust to uncertainties in astrophysics and subgrid models.
The CAMELS project: Expanding the galaxy formation model space with new ASTRID and 28-parameter TNG and SIMBA suites
We present CAMELS-ASTRID, the third suite of hydrodynamical simulations in the Cosmology and Astrophysics with MachinE Learning (CAMELS) project, along with new simulation sets that extend the model parameter space based on the previous frameworks of CAMELS-TNG and CAMELS-SIMBA, to provide broader training sets and testing grounds for machine-learning algorithms designed for cosmological studies.
Robust Field-level Likelihood-free Inference with Galaxies
We train graph neural networks to perform field-level likelihood-free inference using galaxy catalogs from state-of-the-art hydrodynamic simulations of the CAMELS project.
Robust field-level inference with dark matter halos
More importantly, we find that our models are very robust: they can infer the value of $\Omega_{\rm m}$ and $\sigma_8$ when tested using halo catalogues from thousands of N-body simulations run with five different N-body codes: Abacus, CUBEP$^3$M, Enzo, PKDGrav3, and Ramses.
The CAMELS project: public data release
The Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project was developed to combine cosmology with astrophysics through thousands of cosmological hydrodynamic simulations and machine learning.
AI-assisted super-resolution cosmological simulations II: Halo substructures, velocities and higher order statistics
In this work, we expand and test the capabilities of our recently developed super-resolution (SR) model to generate high-resolution (HR) realizations of the full phase-space matter distribution, including both displacement and velocity, from computationally cheap low-resolution (LR) cosmological N-body simulations.
AI-assisted super-resolution cosmological simulations
Cosmological simulations of galaxy formation are limited by finite computational resources.
