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Non-Gaussian information from weak lensing data via deep learning · Around

2018

Non-Gaussian information from weak lensing data via deep learning

Gupta, Arushi, Matilla, José Manuel Zorrilla, Hsu, Daniel et al.

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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 2D convolutional neural network to simulated noiseless lensing maps covering 96 different cosmological models over a range of {$\Omega_m,\sigma_8$}.
Using the area of the confidence contour in the {$\Omega_m,\sigma_8$} plane as a figure-of-merit, derived from simulated convergence maps smoothed on a scale of 1.0 arcmin, we show that the neural network yields $\approx 5 \times$ tighter constraints than the power spectrum, and $\approx 4 \times$ tighter than the lensing peaks.

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