Weak-lensing Mass Reconstruction of Galaxy Clusters with a Convolutional Neural Network

Sungwook E. Hong, Sangnam Park, M. James Jee, Dongsu Bak, Sangjun Cha

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1 Citation (Scopus)

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.

Original languageEnglish
Article number266
JournalAstrophysical Journal
Volume923
Issue number2
DOIs
Publication statusPublished - 2021 Dec 20

Bibliographical note

Funding Information:
Original content from this work may be used under the terms of the . Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. National Research Foundation of Korea (NRF) https://doi.org/10.13039/501100003725 2018R1A6A1A06024977 National Research Foundation of Korea (NRF) https://doi.org/10.13039/501100003725 2017R1A2B2004644 National Research Foundation of Korea (NRF) https://doi.org/10.13039/501100003725 2017R1A4A1015178. National Research Foundation of Korea (NRF) https://doi.org/10.13039/501100003725 2020R1A2B5B01001473 Ministry of Science and ICT, South Korea (MSIT) https://doi.org/10.13039/501100014188 yes � 2021. The Author(s). Published by the American Astronomical Society. Creative Commons Attribution 4.0 licence

Publisher Copyright:
© 2021. The Author(s). Published by the American Astronomical Society.

All Science Journal Classification (ASJC) codes

  • Astronomy and Astrophysics
  • Space and Planetary Science

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