# Non-Gaussian information from weak lensing data via deep learning

@article{Gupta2018NonGaussianIF,
title={Non-Gaussian information from weak lensing data via deep learning},
author={Arushi Gupta and Jos{\'e} Manuel Zorrilla Matilla and Daniel J. Hsu and Zolt{\'a}n Haiman},
journal={ArXiv},
year={2018},
volume={abs/1802.01212}
}
• Published 4 February 2018
• Mathematics
• ArXiv
Weak lensing maps contain information beyond two-point statistics on small scales. Much recent work has tried to extract this information through a range of different observables or via nonlinear transformations of the lensing field. Here we train and apply a two-dimensional convolutional neural network to simulated noiseless lensing maps covering 96 different cosmological models over a range of ${{\mathrm{\ensuremath{\Omega}}}_{m},{\ensuremath{\sigma}}_{8}}$. Using the area of the confidence…

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## References

SHOWING 1-10 OF 102 REFERENCES

### Origin of weak lensing convergence peaks

• Materials Science
• 2016
Weak lensing convergence peaks are a promising tool to probe nonlinear structure evolution at late times, providing additional cosmological information beyond second-order statistics. Previous

### Emulating the CFHTLenS weak lensing data: Cosmological constraints from moments and Minkowski functionals

• Mathematics, Materials Science
• 2015
Weak gravitational lensing is a powerful cosmological probe, with non-Gaussian features potentially containing the majority of the information. We examine constraints on the parameter triplet

### Cosmology constraints from the weak lensing peak counts and the power spectrum in CFHTLenS data

• Physics
• 2015
Lensing peaks have been proposed as a useful statistic, containing cosmological information from non-Gaussianities that is inaccessible from traditional two-point statistics such as the power

### Do dark matter halos explain lensing peaks

• Physics
• 2016
We have investigated a recently proposed halo-based model, Camelus, for predicting weak-lensing peak counts, and compared its results over a collection of 162 cosmologies with those from N-body

### Probing cosmology with weak lensing peak counts

• Physics
• 2010
We propose counting peaks in weak lensing (WL) maps, as a function of their height, to probe models of dark energy and to constrain cosmological parameters. Because peaks can be identified in

### Constraining dark energy by combining cluster counts and shear-shear correlations in a weak lensing survey

• Physics
• 2007
We study the potential of a large future weak lensing survey to constrain dark-energy properties by using both the number counts of detected galaxy clusters (sensitive primarily to density

### The effective number density of galaxies for weak lensing measurements in the LSST project

• Physics
• 2013
Future weak lensing surveys potentially hold the highest statistical power for constraining cosmological parameters compared to other cosmological probes. The statistical power of a weak lensing

### Probing Cosmology with Weak Lensing Minkowski Functionals

• Physics
• 2012
In this paper, we show that Minkowski Functionals (MFs) of weak gravitational lensing (WL) convergence maps contain significant non-Gaussian, cosmology-dependent information. To do this, we use a

### Statistical connection of peak counts to power spectrum and moments in weak-lensing field

The number density of local maxima of weak lensing field, referred to as weak-lensing peak counts, can be used as a cosmological probe. However, its relevant cosmological information is still

### Validity of the Born approximation for beyond Gaussian weak lensing observables

• Physics
• 2016
Accurate forward modeling of weak lensing (WL) observables from cosmological parameters is necessary for upcoming galaxy surveys. Because WL probes structures in the non-linear regime, analytical