Weak-lensing Mass Reconstruction of Galaxy Clusters with a Convolutional Neural Network. II. Application to Next-generation Wide-field Surveys

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 ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>3</mml:mn> <mml:mover> <mml:mrow> <mml:mo>.</mml:mo> </mml:mrow> <mml:mrow> <mml:mtext>°</mml:mtext> </mml:mrow> </mml:mover> <mml:mspace width="0.25em"/> <mml:mn>5</mml:mn> <mml:mo>×</mml:mo> <mml:mn>3</mml:mn> <mml:mover> <mml:mrow> <mml:mo>.</mml:mo> </mml:mrow> <mml:mrow> <mml:mtext>°</mml:mtext> </mml:mrow> </mml:mover> <mml:mspace width="0.25em"/> <mml:mn>5</mml:mn> </mml:math> ) 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.