Publication Abstracts

Way and Klose 2012

Way, M.J., and C.D. Klose, 2012: Can self-organizing maps accurately predict photometric redshifts? Publ. Astron. Soc. Pacific, 124, 274-279, doi:10.1086/664796.

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 Δ = zphot - zspec) 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.

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BibTeX Citation

  author={Way, M. J. and Klose, C. D.},
  title={Can self-organizing maps accurately predict photometric redshifts?},
  journal={Publ. Astron. Soc. Pacific},

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RIS Citation

ID  - wa02400t
AU  - Way, M. J.
AU  - Klose, C. D.
PY  - 2012
TI  - Can self-organizing maps accurately predict photometric redshifts?
JA  - Publ. Astron. Soc. Pacific
VL  - 124
SP  - 274
EP  - 279
DO  - 10.1086/664796
ER  -

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