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Spherical convergence maps dataset

Raphael Sgier; Tomasz Kacprzak; Nathanaël Perraudin; Michaël Defferrard

This dataset is composed of 60 spherical convergence maps (30 per class), separated into a training set of 40 maps and a testing set of 20 maps.

As a preprocessing, we recommend to remove the mean of each map and to smooth them with a Gaussian symmetric beam of 3 arcmins (sphtfunc.smoothing in Healpy).

This dataset was created by R. Sgier and T. Kacprzak based on the work in paper by Sgier R. et al. 2018, "Fast Generation of Covariance Matrices for Weak Lensing", arxiv.org/abs/1801.05745.

This dataset is used in "DeepSphere: Efficient spherical Convolutional Neural Network with HEALPix sampling for cosmological applications" arxiv.org/abs/1810.12186.

Restricted Access

You may request access to the files in this upload, provided that you fulfil the conditions below. The decision whether to grant/deny access is solely under the responsibility of the record owner.


When requesting access, please describe the scope of the project for which the data is intended to be used. The access, if granted, will be only for use within the described scope.

Upon publication of results using this dataset, please cite the two following papers:

Sgier R. et al. 2018, "Fast Generation of Covariance Matrices for Weak Lensing", arxiv.org/abs/1801.05745.

Perraudin et al. 2018, "DeepSphere: Efficient spherical Convolutional Neural Network with HEALPix sampling for cosmological applications" arxiv.org/abs/1810.12186.


  • Perraudin N. et al. 2018, DeepSphere: Efficient spherical Convolutional Neural Network with HEALPix sampling for cosmological applications, https://arxiv.org/abs/1810.12186
  • Sgier R. et al. 2018, Fast Generation of Covariance Matrices for Weak Lensing, https://arxiv.org/abs/1801.05745
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