Weak-lensing Mass Reconstruction of Galaxy Clusters with Convolutional Neural Network
arXiv:2102.05403 · doi:10.3847/1538-4357/ac3090
Abstract
We introduce a novel method for reconstructing the projected matter distributions of galaxy clusters with weak-lensing (WL) data based on convolutional neural network (CNN). Training datasets 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 multi-layered 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 cluster masses in a model-independent way for future wide-field WL surveys.
arXiv comment18 pages, 13 figures, ApJ accepted
References in corpus (37)
- SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
- Astropy: A Community Python Package for Astronomy
- The Structure of Cold Dark Matter Halos
- Deep Residual Learning for Image Recognition
- LSST: from Science Drivers to Reference Design and Anticipated Data Products
- Weak Gravitational Lensing
- Focal Loss for Dense Object Detection
- Phase recovery and holographic image reconstruction using deep learning in neural networks
- Wide-Field InfrarRed Survey Telescope-Astrophysics Focused Telescope Assets WFIRST-AFTA 2015 Report
- Constraints on the Self-Interaction Cross-Section of Dark Matter from Numerical Simulations of the Merging Galaxy Cluster 1E 0657-5
- Dark Energy Survey Year 1 Results: Cosmological Constraints from Cosmic Shear
- A Review of Convolutional Neural Networks for Inverse Problems in Imaging
- Cosmology from cosmic shear power spectra with Subaru Hyper Suprime-Cam first-year data
- Probing the Universe with Weak Lensing
- Ray Tracing Simulations of Weak Lensing by Large-Scale Structure
- Weighing the Giants - I. Weak-lensing masses for 51 massive galaxy clusters: project overview, data analysis methods and cluster images
- Star-galaxy Classification Using Deep Convolutional Neural Networks
- Photometric redshifts from SDSS images using a Convolutional Neural Network
- Masses of galaxy clusters from gravitational lensing
- GREAT3 results I: systematic errors in shear estimation and the impact of real galaxy morphology
- Discovery of a Ringlike Dark Matter Structure in the Core of the Galaxy Cluster Cl 0024+17
- Noise properties of gravitational lens mass reconstruction
- MassiveNuS: Cosmological Massive Neutrino Simulations
- The Mass distribution of the Most Luminous X-ray Cluster RXJ1347.5-1145 from Gravitational Lensing
- Weighing "El Gordo" with a Precision Scale: Hubble Space Telescope Weak-lensing Analysis of the Merging Galaxy Cluster ACT-CL J0102-4915 at z=0.87
- Cosmic shear covariance: The log-normal approximation
- Deep Convolutional Neural Networks as strong gravitational lens detectors
- FASTLens (FAst STatistics for weak Lensing) : Fast method for Weak Lensing Statistics and map making
- Unbiased Cluster Lens Reconstruction
- Testing the lognormality of the galaxy and weak lensing convergence distributions from Dark Energy Survey maps
- Mass-sheet degeneracy: Fundamental limit on the cluster mass reconstruction from statistical (weak) lensing
- MC: Subaru and Hubble Space Telescope Weak-Lensing Analysis of the Double Radio Relic Galaxy Cluster PLCK G287.0+32.9
- Impact of Atmospheric Chromatic Effects on Weak Lensing Measurements
- Multi-wavelength Analysis of the Merging Galaxy Cluster A115
- Pixelation Effects in Weak Lensing
- Toward Solving the Puzzle: Dissecting the Complex Merger A521 with Multi-wavelength Data
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- The Three Hundred Project: Mapping The Matter Distribution in Galaxy Clusters Via Deep Learning from Multiview Simulated Observations
- Cosmic topology. Part IVa. Classification of manifolds using machine learning: a case study with small toroidal universes
- Weak-lensing Mass Reconstruction of Galaxy Clusters with a Convolutional Neural Network -- II: Application to Next-Generation Wide-Field Surveys