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Found 4 papers, 1 papers with code
DeepLSS: breaking parameter degeneracies in large scale structure with deep learning analysis of combined probes
Galaxy bias $b_g$ is improved by 1. 5x, stochasticity $r_g$ by 3x, and the redshift evolution $\eta_{\rm{IA}}$ and $\eta_b$ by 1. 6x.
Cosmological constraints with deep learning from KiDS-450 weak lensing maps
We present the cosmological results with a CNN from the KiDS-450 tomographic weak lensing dataset, constraining the total matter density $\Omega_m$, the fluctuation amplitude $\sigma_8$, and the intrinsic alignment amplitude $A_{\rm{IA}}$.
Cosmology and Nongalactic Astrophysics
Cosmological constraints from noisy convergence maps through deep learning
We find that, for a shape noise level corresponding to 8. 53 galaxies/arcmin$^2$ and the smoothing scale of $\sigma_s = 2. 34$ arcmin, the network is able to generate 45% tighter constraints.
Cosmology and Nongalactic Astrophysics
Fast cosmic web simulations with generative adversarial networks
Computational models of the underlying physical processes, such as classical N-body simulations, are extremely resource intensive, as they track the action of gravity in an expanding universe using billions of particles as tracers of the cosmic matter distribution.
