Predicting photometric redshifts with polynomial regression

Details Predicting photometric redshifts with polynomial regression Predicting photometric redshifts with polynomial regression

Aug 2008

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2008spie.7019e.110w&link_type=abstract

Advanced Software and Control for Astronomy II. Edited by Bridger, Alan; Radziwill, Nicole M. Proceedings of the SPIE, Volume 70

Astronomy and Astrophysics

Astronomy

Scientific paper

The Sloan Digital Sky Survey (SDSS) is an ambitious photometry and spectra project, providing huge and abundant samples for photometric redshift estimation. We employ polynomial regression to estimate photometric redshifts using 330,000 galaxies with known spectroscopic redshifts from SDSS Release Four spectroscopic catalog, and compare three polynomial regressionmethods, i.e. linear regression, quadratic regression and cubic regression with different samples. This technique gives absolute convergence in a finite number of steps, represents better fit with fewer coefficients and yields the result as a mathematical expression. This method is much easier to use and understand than other empirical methods for astronomers. Our result indicates that equally or more powerful accuracy is provided, moreover, the best r.m.s. dispersion of this approach is 0.0256. In addition, the comparison between our results with other works is addressed.

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