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Found 10 papers, 3 papers with code
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
PynPoint: a modular pipeline architecture for processing and analysis of high-contrast imaging data
The architecture of PynPoint has been redesigned with the core functionalities decoupled from the pipeline modules.
Earth and Planetary Astrophysics Instrumentation and Methods for 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
Lenstronomy: multi-purpose gravitational lens modelling software package
The software is also able to reconstruct complex extended sources (Birrer et.
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.
Approximate Bayesian Computation for Forward Modeling in Cosmology
We first review the principles of ABC and discuss its implementation using a Population Monte-Carlo (PMC) algorithm and the Mahalanobis distance metric.
Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics Computation
Systematic Bias in Cosmic Shear: Beyond the Fisher Matrix
For a future DUNE-like full sky survey, we find that, for cases with mild redshift evolution, the variance of the additive systematic signal should be kept below 10^-7 to ensure biases on cosmological parameters that are sub-dominant to the statistical errors.
