The Efficacy of Galaxy Shape Parameters in Photometric Redshift Estimation: A Neural Network Approach
We present a determination of the effects of including galaxy morphological parameters in photometric redshift estimation with an artificial neural network method. Neural networks, which recognize patterns in the information content of data in an unbiased way, can be a useful estimator of the additional information contained in extra parameters, such as those describing morphology, if the input data are treated on an equal footing. We use imaging and five band photometric magnitudes from the All-wavelength Extended Groth Strip International Survey. It is shown that certain principal components of the morphology information are correlated with galaxy type. However, we find that for the data used the inclusion of morphological information does not have a statistically significant benefit for photometric redshift estimation with the techniques employed here. The inclusion of these parameters may result in a trade-off between extra information and additional noise, with the additional noise becoming more dominant as more parameters are added.
Copyright © 2011 Astronomical Society of the Pacific. Article first published in PASP: Publications of the Astronomical Society of the Pacific 123, no. 903 (April 8, 2011): 615-21. doi:10.1086/660155.
The definitive version is available at:
Singal, J., M. Shmakova, B. Gerke, R. L. Griffith, and J. Lotz. "The Efficacy of Galaxy Shape Parameters in Photometric Redshift Estimation: A Neural Network Approach."Publications of the Astronomical Society of the Pacific 123, no. 903 (April 8, 2011): 615-21. doi:10.1086/660155.
Singal, Jack; Shmakova, M.; Gerke, B.; Griffith, R. L.; and Lotz, J., "The Efficacy of Galaxy Shape Parameters in Photometric Redshift Estimation: A Neural Network Approach" (2011). Physics Faculty Publications. 12.