Deep Learning galaxy cluster's structural parameters from Weak Lensing observations

Abstract

Galaxy clusters are the most massive gravitationally bound structures in the Universe and key probes of cosmic evolution. The large data volume expected from upcoming surveys requires efficient automated analysis methods for tens of thousands of clusters. We present a study using Convolutional Neural Networks (CNNs) to infer cluster structural parameters from weak gravitational lensing observations. Three architectures (VGG-Net, Inception-v4, Inception-ResNet-v2) were implemented in PyTorch and trained on 75,000 synthetic reduced shear maps generated with MOKA, simulating galaxy clusters at $z = 0.25$. The networks simultaneously predict five parameters: virial mass, NFW concentrations, substructure count, and smooth component mass fraction. Tests on 5000 clusters show high accuracy for primary properties. With realistic noise ($n_{\rm gal}=30$, $\sigma_{\epsilon}=0.3$), mass predictions remain robust (RMS $\sim 1.02 \times 10^{14}$ M$_\odot$/h, $\sim20$% deviation). Concentration estimates are stable, with VGG-22 achieving the lowest RMS. Substructure count properties are more challenging, with systematic underestimation across models, while the smooth component mass fraction is consistently well recovered, indicating strong robustness against noise. Comparison with traditional shear profile fitting shows improved CNN performance. VGG-22 achieves near-unbiased mass estimates and significantly better concentration recovery, reducing systematic errors. These results demonstrate that CNNs provide an effective and scalable alternative to traditional methods, particularly suited for large survey datasets.[Abridged]