Cluster Membership of Galaxies Using Multi-Layer Perceptron Neural Network

Yasuhiro Hashimoto*, Cheng Han Liu

*此作品的通信作者

研究成果: 雜誌貢獻期刊論文同行評審

摘要

In this study, we report systematic investigations of the membership of galaxies inside a cluster using a machine learning (ML) neural network. By directly assigning the membership, rather than estimating the galaxy redshift as an intermediate step, we optimize the network structure to determine the membership classification. The cluster membership is determined by the Multi-Layer Perceptron (MLP) neural network trained using various observed photometric and morphological parameters of galaxies measured from I and V band images taken with the Subaru Suprime-Cam of 16 clusters at redshift ∼0.15–0.3. This dataset enables MLP to be applied to cluster galaxies in a wide range of cluster-centric distances, well into a field, and a wide range of galaxy magnitudes, into a regime of dwarf galaxies. We find: (1) With only two bands, our MLP model can achieve relatively high overall performance, obtaining high scores simultaneously in both the purity and the completeness of the classification; (2) The performance of MLP can be improved by including non-SED (Spectral Energy Distribution) parameters; (3) Faint galaxies are harder to assign their memberships even using our MLP model, though the performance is more robust than other photometric methods. ML can effectively combine various conventional methods of finding cluster membership, making it inherit advantages of each method. The overall good performance of the ML membership is vital to cluster studies in the era of faint and data-intensive galaxy survey in which the complete spectroscopic observation is out of reach.

原文英語
文章編號339
期刊Universe
8
發行號7
DOIs
出版狀態已發佈 - 2022 7月

ASJC Scopus subject areas

  • 物理與天文學 (全部)

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