1. Weak-lensing Mass Reconstruction of Galaxy Clusters with a Convolutional Neural Network. II. Application to Next-generation Wide-field Surveys
- Author
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Sangjun Cha, M. James Jee, Sungwook E. Hong, Sangnam Park, Dongsu Bak, and Taehwan Kim
- Subjects
Weak gravitational lensing ,Dark matter distribution ,Galaxy clusters ,Convolutional neural networks ,Astrophysics ,QB460-466 - Abstract
Traditional weak-lensing mass reconstruction techniques suffer from various artifacts, including noise amplification and the mass-sheet degeneracy. In S. E. Hong et al., 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 (WF) observations. Assuming the field of view ( $3\mathop{.}\limits^{\unicode{x000b0}}\,5\times 3\mathop{.}\limits^{\unicode{x000b0}}\,5$ ) and depth (27 mag at 5 σ ) of the Vera C. Rubin Observatory, we generated training data sets 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 ∼75% completeness down to ∼10 ^14 M _⊙ . We anticipate that this CNN-based mass reconstruction will be a powerful tool in the Rubin era, enabling fast and robust WF mass reconstructions on a routine basis. more...
- Published
- 2025
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