# 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} }

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…

## 56 Citations

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A new training strategy to train the neural network with noisy data is presented, as well as considerations for practical applications of the deep learning approach.

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