Accession Number N20120011153
Title Can Selforganizing Maps Accurately Predict Photometric Redshifts.
Publication Date Mar 2012
Media Count 5p
Personal Author C. Klose M. J. Way
Abstract We present an unsupervised machine-learning approach that can be employed for estimating photometric redshifts. The proposed method is based on a vector quantization called the self-organizing-map (SOM) approach. A variety of photometrically derived input values were utilized from the Sloan Digital Sky Survey's main galaxy sample, luminous red galaxy, and quasar samples, along with the PHAT0 data set from the Photo-z Accuracy Testing project. Regression results obtained with this new approach were evaluated in terms of root-mean-square error (RMSE) to estimate the accuracy of the photometric redshift estimates. The results demonstrate competitive RMSE and outlier percentages when compared with several other popular approaches, such as artificial neural networks and Gaussian process regression. SOM RMSE results (using delta(z) = z(sub phot) - z(sub spec)) are 0.023 for the main galaxy sample, 0.027 for the luminous red galaxy sample, 0.418 for quasars, and 0.022 for PHAT0 synthetic data. The results demonstrate that there are nonunique solutions for estimating SOM RMSEs. Further research is needed in order to find more robust estimation techniques using SOMs, but the results herein are a positive indication of their capabilities when compared with other well-known methods.
Keywords Astronomical maps
Astronomical photometry
Error analysis
Neural nets
Red shift
Root-mean-square errors
Self organizing systems
Sky surveys(Astronomy)

Source Agency National Aeronautics and Space Administration
NTIS Subject Category 54B - Astronomy & Celestial Mechanics
Corporate Author National Aeronautics and Space Administration, Moffett Field, CA. Ames Research Center.
Document Type Journal article
Title Note N/A
NTIS Issue Number 1226
Contract Number N/A

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