We present the results of using a custom support vector machine regression method to determine the effects of including galaxy morphological parameters in photometric redshift estimation. We also present a comparison with other methods. Support vector machine algorithms can be a useful estimator of the additional information contained in parameters, such as those describing morphology, because they utilize the information content of data in a way that can treat different input types symmetrically. We use a set of 2600 galaxies with imaging and five band photometric magnitudes with known spectroscopic redshifts as test data to evaluate the estimation.
American Astronomical Society Meeting Abstracts #227
- Pub Date:
- January 2016