TY - JOUR
T1 - Cluster Membership of Galaxies Using Multi-Layer Perceptron Neural Network
AU - Hashimoto, Yasuhiro
AU - Liu, Cheng Han
N1 - Funding Information:
This research has made use of the NASA/IPAC Extragalactic Database (NED), which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration. This research is based in part on data col-lected at Subaru Telescope, which is operated by the National Astronomical Observatory of Japan. We are honored and grateful for the opportunity of observing the Universe from Maunakea, which has a cultural, historical, and natural significance in Hawaii. SDSS-IV is managed by the Astrophysical Research Consortium for the Participating Institutions of the SDSS Collaboration including the Brazil-ian Participation Group, the Carnegie Institution for Science, Carnegie Mellon University, Center for Astrophysics|Harvard & Smithsonian, the Chilean Participation Group, the French Participation Group, Instituto de Astrofísica de Canarias, The Johns Hopkins University, Kavli Institute for the Physics and Mathematics of the Universe (IPMU)/University of Tokyo, the Korean Participation Group, Lawrence Berkeley National Laboratory, Leibniz Institut für Astrophysik Potsdam (AIP), Max-Planck-Institut für Astronomie (MPIA Heidelberg), Max-Planck-Institut für Astrophysik (MPA Garching), Max-Planck-Institut für Extraterrestrische Physik (MPE), National Astronomical Obser-vatories of China, New Mexico State University, New York University, University of Notre Dame, Observatário Nacional/MCTI, The Ohio State University, Pennsylvania State University, Shanghai Astronomical Observatory, United Kingdom Participation Group, Universidad Nacional Autónoma de México, University of Arizona, University of Colorado Boulder, University of Oxford, University of Portsmouth, University of Utah, University of Virginia, University of Washington, University of Wisconsin, Vanderbilt University, and Yale University.
Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2022/7
Y1 - 2022/7
N2 - 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.
AB - 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.
KW - galaxies:clusters:general
KW - galaxies:distances and redshifts
KW - galaxies:general
KW - methods:data analysis
KW - methods:miscellaneous
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U2 - 10.3390/universe8070339
DO - 10.3390/universe8070339
M3 - Article
AN - SCOPUS:85133192297
SN - 2218-1997
VL - 8
JO - Universe
JF - Universe
IS - 7
M1 - 339
ER -