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Found 13 papers, 5 papers with code
Cosmology from Galaxy Redshift Surveys with PointNet
In this work, we aim to improve upon two-point statistics by employing a \textit{PointNet}-like neural network to regress the values of the cosmological parameters directly from point cloud data.
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.
Emulation of cosmological mass maps with conditional generative adversarial networks
Weak gravitational lensing mass maps play a crucial role in understanding the evolution of structures in the universe and our ability to constrain cosmological models.
Cosmological N-body simulations: a challenge for scalable generative models
Our results show that the proposed model produces samples of high visual quality, although the statistical analysis reveals that capturing rare features in the data poses significant problems for the generative models.
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
DeepSphere: towards an equivariant graph-based spherical CNN
Spherical data is found in many applications.
DeepSphere: Efficient spherical Convolutional Neural Network with HEALPix sampling for cosmological applications
We present a spherical CNN for analysis of full and partial HEALPix maps, which we call DeepSphere.
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.
Fast Point Spread Function Modeling with Deep Learning
We find that our approach is able to accurately reproduce the SDSS PSF at the pixel level, which, due to the speed of both the model evaluation and the parameter estimation, offers good prospects for incorporating our method into the $MCCL$ framework.
Accelerating Approximate Bayesian Computation with Quantile Regression: Application to Cosmological Redshift Distributions
This model is trained on a small number of simulations and estimates which regions of the prior space are likely to be accepted into the posterior.
Cosmological model discrimination with Deep Learning
We find that our implementation of DCNN outperforms the skewness and kurtosis statistics, especially for high noise levels.
GalSim: The modular galaxy image simulation toolkit
GALSIM is a collaborative, open-source project aimed at providing an image simulation tool of enduring benefit to the astronomical community.
Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics 85-04
