2021/02/28 by Sungwook E. Hong, Sangnam Park, M. James Jee +2 · 10 citations
Biochemistry, Genetics and Molecular Biology · Physics and Astronomy · #Adaptive optics and wavefront sensing #Advanced Fluorescence Microscopy Techniques #Algorithm #Artificial intelligence #Artificial neural network #Astrophysics #Cluster (spacecraft) #Computer science #Convolution (computer science) #Convolutional neural network #Data mining #Degeneracy (biology) #Galaxies: Formation, Evolution, Phenomena #Galaxy #Image (mathematics) #Measure (data warehouse) #Noise (video) #Pattern recognition (psychology) #Physics #Pixel #Redshift #Weak gravitational lensing #astro-ph.CO #astro-ph.IM
paper · pdf · open access · doi:10.3847/1538-4357/ac3090
published in The Astrophysical Journal 923(2), 266 (IOP Publishing) · 18 pages, 13 figures, ApJ accepted
openalex publication_date 2021/12/01 · arxiv created 2021/12/14 · arxiv updated 2021/12/30 · openalex created_date 2021/12/31 · openalex updated_date 2026/08/06
Abstract We introduce a novel method for reconstructing the projected matter distributions of galaxy clusters with weak-lensing (WL) data based on a convolutional neural network (CNN). Training data sets 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 multilayered CNN with the architecture of alternating convolution and trans-convolution filters significantly outperforms the traditional reconstruction methods. The CNN method provides better pixel-to-pixel correlations with the truth, restores more accurate positions of the mass peaks, and more efficiently suppresses artifacts near the field edges. In addition, the CNN mass reconstruction lifts the mass-sheet degeneracy when applied to our projected cluster mass estimation from sufficiently large fields. This implies that this CNN algorithm can be used to measure the cluster masses in a model-independent way for future wide-field WL surveys.