Volume 576, April 2015
|Number of page(s)||14|
|Section||Numerical methods and codes|
|Published online||20 April 2015|
Determining spectroscopic redshifts by using k nearest neighbor regression
I. Description of method and analysis
Heidelberger Institut für Theoretische Studien (HITS), Schloss-Wolfsbrunnenweg 35 69118 Heidelberg Germany
Received: 13 August 2014
Accepted: 6 February 2015
Context. In astronomy, new approaches to process and analyze the exponentially increasing amount of data are inevitable. For spectra, such as in the Sloan Digital Sky Survey spectral database, usually templates of well-known classes are used for classification. In case the fitting of a template fails, wrong spectral properties (e.g. redshift) are derived. Validation of the derived properties is the key to understand the caveats of the template-based method.
Aims. In this paper we present a method for statistically computing the redshift z based on a similarity approach. This allows us to determine redshifts in spectra for emission and absorption features without using any predefined model. Additionally, we show how to determine the redshift based on single features. As a consequence we are, for example, able to filter objects that show multiple redshift components.
Methods. The redshift calculation is performed by comparing predefined regions in the spectra and individually applying a nearest neighbor regression model to each predefined emission and absorption region.
Results. The choice of the model parameters controls the quality and the completeness of the redshifts. For ≈90% of the analyzed 16 000 spectra of our reference and test sample, a certain redshift can be computed that is comparable to the completeness of SDSS (96%). The redshift calculation yields a precision for every individually tested feature that is comparable to the overall precision of the redshifts of SDSS. Using the new method to compute redshifts, we could also identify 14 spectra with a significant shift between emission and absorption or between emission and emission lines. The results already show the immense power of this simple machine-learning approach for investigating huge databases such as the SDSS.
Key words: methods: data analysis / astronomical databases: miscellaneous / methods: statistical / galaxies: distances and redshifts / catalogs
© ESO, 2015
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