Year: 2019
Type: article
Abstract: Classification of intermediate redshift (z = 0.3–0.8) emission line galaxies as star-forming galaxies, composite galaxies, active galactic nuclei (AGNs), or low-ionization nuclear emission regions (LINERs) using optical spectra alone was impossible because the lines used for standard optical diagnostic diagrams: [N ii], Hα, and [S ii] are redshifted out of the observed wavelength range. In this work, we address this problem using four supervised machine-learning classification algorithms: k-nearest neighbors (KNN), support vector classifier (SVC), random forest (RF), and a multilayer perceptron (MLP) neural network. For input features, we use properties that can be measured from optical galaxy spectra out to z < 0.8—[O iii]/Hβ, [O ii]/Hβ, [O iii] line width, and stellar velocity dispersion—and four colors (u − g, g − r, r − i, and i − z) corrected to z = 0.1. The labels for the low redshift emission line galaxy training set are determined using standard optical diagnostic diagrams. RF has the best area under curve score for classifying all four galaxy types, meaning the highest distinguishing power. Both the AUC scores and accuracies of the other algorithms are ordered as MLP > SVC > KNN. The classification accuracies with all eight features (and the four spectroscopically determined features only) are 93.4% (92.3%) for star-forming galaxies, 69.4% (63.7%) for composite galaxies, 71.8% (67.3%) for AGNs, and 65.7% (60.8%) for LINERs. The stacked spectrum of galaxies of the same type as determined by optical diagnostic diagrams at low redshift and RF at intermediate redshift are broadly consistent. Our publicly available code ( https://github.com/zkdtc/MLC_ELGs ) and trained models will be instrumental for classifying emission line galaxies in upcoming wide-field spectroscopic surveys.
Source: The Astrophysical Journal
Authors: Kai Zhang, David J. Schlegel, Brett H. Andrews, Johan Comparat, Christoph Schäfer, Jose Antonio Vazquez Mata, Jean-Paul Kneib, Renbin Yan
Institutions: Lawrence Berkeley National Laboratory, University of Pittsburgh, Consejo Superior de Investigaciones Científicas, Max Planck Institute for Extraterrestrial Physics, Instituto de Física Teórica, Universidad Autónoma de Madrid, École Polytechnique Fédérale de Lausanne, Universidad Nacional Autónoma de México, Centre National de la Recherche Scientifique, Aix-Marseille Université, Laboratoire d’Astrophysique de Marseille, University of Kentucky
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: bronze
Funders: U.S. Department of Energy, Alfred P. Sloan Foundation, Yale University, Carnegie Institution of Washington, Vanderbilt University, Ohio State University, University of Washington, Johns Hopkins University, Carnegie Mellon University, University of Notre Dame, University of Portsmouth, New Mexico State University, York University, University of Oxford, Ministério da Ciência, Tecnologia e Inovação, Universidad Nacional Autónoma de México, Max-Planck-Institut für Astrophysik, Max-Planck-Institut für Astronomie, Instituto de Astrofísica de Canarias, Office of Science, University of Colorado Boulder, Smithsonian Astrophysical Observatory, Leibniz-Gemeinschaft, Lawrence Berkeley National Laboratory
OpenAlex ID: W2969767295