Weak-lensing Mass Reconstruction of Galaxy Clusters with Convolutional Neural Network

Understand

We introduce a novel method for reconstructing the projected matter distributions of galaxy clusters with weak-lensing (WL) data based on convolutional neural network (CNN).

• Training datasets are generated with ray-tracing through cosmological simulations.
• We control the noise level of the galaxy shear catalog such that it mimics the typical properties of the existing ground-based WL observations of galaxy clusters.
• We find that the mass reconstruction by our multi-layered CNN with the architecture of alternating convolution and trans-convolution filters significantly outperforms the traditional reconstruction methods.