Year: 2021
Type: article
Abstract: Context. Defining templates of galaxy spectra is useful to quickly characterise new observations and organise databases from surveys. These templates are usually built from a pre-defined classification based on other criteria. Aims. We present an unsupervised classification of 702 248 spectra of galaxies and quasars with redshifts smaller than 0.25 that were retrieved from the Sloan Digital Sky Survey (SDSS) database, release 7. Methods. The spectra were first corrected for redshift, then wavelet-filtered to reduce the noise, and finally binned to obtain about 1437 wavelengths per spectrum. The unsupervised clustering algorithm Fisher-EM, relying on a discriminative latent mixture model, was applied on these corrected spectra. The full set and several subsets of 100 000 and 300 000 spectra were analysed. Results. The optimum number of classes given by a penalised likelihood criterion is 86 classes, of which the 37 most populated gather 99% of the sample. These classes are established from a subset of 302 214 spectra. Using several cross-validation techniques we find that this classification agrees with the results obtained on the other subsets with an average misclassification error of about 15%. The large number of very small classes tends to increase this error rate. In this paper, we do an initial quick comparison of our classes with literature templates. Conclusions. This is the first time that an automatic, objective and robust unsupervised classification is established on such a large number of galaxy spectra. The mean spectra of the classes can be used as templates for a large majority of galaxies in our Universe.
Source: Astronomy and Astrophysics
Authors: D. Fraix-Burnet, C. Bouveyron, J. Moultaka
Institutions: Centre National de la Recherche Scientifique, Institut de Planétologie et d'Astrophysique de Grenoble, Université Grenoble Alpes, Institut national de recherche en sciences et technologies du numérique, Université Côte d'Azur, Laboratoire Jean-Alexandre Dieudonné, MAASAI: Modèles et algorithmes pour l'intelligence artificielle, Université Toulouse III - Paul Sabatier, Centre National d'Études Spatiales, Institut de Recherche en Astrophysique et Planétologie, Université de Toulouse
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,550
Funders: National Science Foundation, U.S. Department of Energy, National Aeronautics and Space Administration, Alfred P. Sloan Foundation, Princeton University, University of Chicago, University of Washington, Johns Hopkins University, University of Pittsburgh, New Mexico State University, Agence Nationale de la Recherche, Max-Planck-Gesellschaft, Max-Planck-Institut für Astronomie, U.S. Naval Observatory, Fermilab, Los Alamos National Laboratory
Awards: 3IA Côte d'Azur, G3010527862
OpenAlex ID: W3135097080