An approach to the analysis of SDSS spectroscopic outliers based on self-organizing maps

Designing the outlier analysis software package for the next Gaia survey

¹ Universidade da A Coruña (UDC), Fac. Informática, Campus de Elviña, 15071 A Coruña, Spain
e-mail: dfustes@udc.es; manteiga@udc.es; dafonte@udc.es; cibarcay@udc.es
² Universidade de Vigo (Uvigo), Dept. Física Aplicada, Campus Lagoas-Marcosende s/n, 36310 Vigo, Spain
e-mail: ulla@uvigo.es
³ Max Planck Institute For Astronomy (MPIA), Knigstuhl 17, 69117 Heidelberg, Germany
e-mail: smith@mpia-hd.mpg.de
⁴ Universitat de Barcelona (UB), Dept. Astronomia i Meteorologia ICCUB-IEEC, Martí Franquès 1, Barcelona, Spain
e-mail: rborrachero@am.ub.es
⁵ Osservatorio Astronomico di Padova (INAF), Vicolo Osservatorio 5, Padova, Italy
e-mail: rosanna.sordo@oapd.inaf.it

Received: 11 March 2013
Accepted: 31 August 2013

Abstract

Aims. A new method applied to the segmentation and further analysis of the outliers resulting from the classification of astronomical objects in large databases is discussed. The method is being used in the framework of the Gaia satellite Data Processing and Analysis Consortium (DPAC) activities to prepare automated software tools that will be used to derive basic astrophysical information that is to be included in final Gaia archive.

Methods. Our algorithm has been tested by means of simulated Gaia spectrophotometry, which is based on SDSS observations and theoretical spectral libraries covering a wide sample of astronomical objects. Self-organizing maps networks are used to organize the information in clusters of objects, as homogeneously as possible according to their spectral energy distributions, and to project them onto a 2D grid where the data structure can be visualized.

Results. We demonstrate the usefulness of the method by analyzing the spectra that were rejected by the SDSS spectroscopic classification pipeline and thus classified as “UNKNOWN”. First, our method can help distinguish between astrophysical objects and instrumental artifacts. Additionally, the application of our algorithm to SDSS objects of unknown nature has allowed us to identify classes of objects with similar astrophysical natures. In addition, the method allows for the potential discovery of hundreds of new objects, such as white dwarfs and quasars. Therefore, the proposed method is shown to be very promising for data exploration and knowledge discovery in very large astronomical databases, such as the archive from the upcoming Gaia mission.