Physical Sciences and Mathematics Commons^™

Analysis Of A Custom Support Vector Machine For Photometric Redshift Estimation And The Inclusion Of Galaxy Shape Information, Evan Jones, J. Singal

Honors Theses

Aims. We present a custom support vector machine classification package for photometric redshift estimation, including comparisons with other methods. We also explore the efficacy of including galaxy shape information in redshift estimation. Support vector machines, a type of machine learning, utilize optimization theory and supervised learning algorithms to construct predictive models based on the information content of data in a way that can treat different input features symmetrically, which can be a useful estimator of the information contained in additional features beyond photometry, such as those describing the morphology of galaxies.

Methods. The custom support vector machine package we have …