Abstract
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.
Highlights
IntroductionWeak-lensing (WL) is firmly established as the most direct method to measure the mass of an astrophysical object ranging from a galaxy (galaxy-galaxy lensing) to the cosmological large scale structure (cosmic shear)
Weak-lensing (WL) is firmly established as the most direct method to measure the mass of an astrophysical object ranging from a galaxy to the cosmological large scale structure
We focus on systematics arising in galaxy cluster mass reconstruction from WL source catalogs
Summary
Weak-lensing (WL) is firmly established as the most direct method to measure the mass of an astrophysical object ranging from a galaxy (galaxy-galaxy lensing) to the cosmological large scale structure (cosmic shear). A number of issues on WL systematics have been identified, including shear calibration, photometric redshift degeneracy, model bias, mass-sheet degeneracy, astrophysical processes, and so on We focus on systematics arising in galaxy cluster mass reconstruction from WL source catalogs. Very few studies employed the mass reconstruction for quantitative analysis (e.g., derivation of galaxy cluster masses). This is because the current mass reconstruction algorithms suffer from various artifacts. Other critical issues include finite-field effect, ill-posed mathematical inversion, smoothing artifact, field edge systematics, and so on (e.g., Bartelmann 1995; Seitz & Schneider 1996)
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