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The Efficacy Of Galaxy Shape Parameters In Photometric Redshift Estimation: A Neural Network Approach, Jack Singal, M. Shmakova, B. Gerke, R. L. Griffith, J. Lotz

Physics Faculty Publications

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 …