NonGaussian information from weak lensing data via deep learning
Abstract
Weak lensing maps contain information beyond twopoint 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 twodimensional 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 ≈5 × tighter constraints than the power spectrum, and ≈4 × 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, nonGaussian statistics such as lensing peaks.
 Publication:

Physical Review D
 Pub Date:
 May 2018
 DOI:
 10.1103/PhysRevD.97.103515
 arXiv:
 arXiv:1802.01212
 Bibcode:
 2018PhRvD..97j3515G
 Keywords:

 Astrophysics  Cosmology and Nongalactic Astrophysics;
 Computer Science  Machine Learning;
 Statistics  Machine Learning
 EPrint:
 15 pages, 13 figures, accepted to PRD