Weak-lensing Mass Reconstruction of Galaxy Clusters with a Convolutional Neural Network -- II: Application to Next-Generation Wide-Field Surveys

TL;DR

This paper demonstrates that a convolutional neural network can effectively reconstruct galaxy cluster mass maps from weak-lensing data, outperforming traditional methods, especially for next-generation wide-field surveys like the Rubin Observatory.

Contribution

The paper introduces an improved CNN-based method for weak-lensing mass reconstruction, tailored for next-generation surveys, showing high accuracy and cluster detection efficiency.

Findings

High-fidelity mass reconstructions consistent with true convergence fields.

Achieved ~75% completeness for clusters down to 10^14 solar masses.

Effective reconstruction of both small and large-scale structures.

Abstract

Traditional weak-lensing mass reconstruction techniques suffer from various artifacts, including noise amplification and the mass-sheet degeneracy. In Hong et al. (2021), we demonstrated that many of these pitfalls of traditional mass reconstruction can be mitigated using a deep learning approach based on a convolutional neural network (CNN). In this paper, we present our improvements and report on the detailed performance of our CNN algorithm applied to next-generation wide-field observations. Assuming the field of view ($3\deg.5 \times 3\deg.5$) and depth (27 mag at $5\sigma$) of the Vera C. Rubin Observatory, we generated training datasets of mock shear catalogs with a source density of 33 arcmin$^{-2}$ from cosmological simulation ray-tracing data. We find that the current CNN method provides high-fidelity reconstructions consistent with the true convergence field, restoring both small and large-scale structures. In addition, the cluster detection utilizing our CNN reconstruction achieves $\sim75$% completeness down to $\sim 10^{14}M_{\odot}$. We anticipate that this CNN-based mass reconstruction will be a powerful tool in the Rubin era, enabling fast and robust wide-field mass reconstructions on a routine basis.