Non-Gaussian information from weak lensing data via deep learning
This provides stronger cosmological constraints for astrophysics research, though it is incremental as it builds on existing deep learning methods applied to a known problem.
The paper tackled extracting non-Gaussian information from weak lensing maps to improve cosmological parameter constraints, achieving approximately 5 times tighter constraints on parameters like Ω_m and σ_8 compared to the power spectrum and 4 times tighter than lensing peaks.
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 {$Ω_m,σ_8$}. Using the area of the confidence contour in the {$Ω_m,σ_8$} 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. Such gains illustrate the extent to which weak lensing data encode cosmological information not accessible to the power spectrum or even other, non-Gaussian statistics such as lensing peaks.