The Efficacy of Galaxy Shape Parameters in Photometric Redshift Estimation: A Neural Network Approach

TL;DR

This study investigates whether adding galaxy morphological parameters improves photometric redshift estimates using neural networks, finding limited benefits due to noise trade-offs.

Contribution

It demonstrates that morphological parameters do not significantly enhance neural network-based photometric redshift estimation with the given data.

Findings

Morphological parameters correlate with galaxy type.

Adding morphology does not significantly improve redshift accuracy.

Extra parameters may introduce noise outweighing benefits.

Abstract

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.