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Found 11 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.
Super-resolving Dark Matter Halos using Generative Deep Learning
Generative deep learning methods built upon Convolutional Neural Networks (CNNs) provide a great tool for predicting non-linear structure in cosmology.
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 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.
Radio frequency interference mitigation using deep convolutional neural networks
We employ a special type of Convolutional Neural Network, the U-Net, that enables the classification of clean signal and RFI signatures in 2D time-ordered data acquired from a radio telescope.
Instrumentation and Methods for Astrophysics
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.
The Stability of the Point Spread Function of the Advanced Camera for Surveys on the Hubble Space Telescope and Implications for Weak Gravitational Lensing
We derive a parametric correction for the effect of CTE on the shapes of objects in the COSMOS field as a function of observation date, position within the ACS WFC field, and object flux.
Polar Shapelets
The shapelets method for image analysis is based upon the decomposition of localised objects into a series of orthogonal components with convenient mathematical properties.
Weak Lensing Measurements: A Revisited Method and Application to HST Images
The weak distortions produced by gravitational lensing in the images of background galaxies provide a method to measure directly the distribution of mass in the universe.
