SOMz: photometric redshift PDFs with self organizing maps and random atlas

18 Dec 2013M. Carrasco KindR. J. Brunner

In this paper we explore the applicability of the unsupervised machine learning technique of Self Organizing Maps (SOM) to estimate galaxy photometric redshift probability density functions (PDFs). This technique takes a spectroscopic training set, and maps the photometric attributes, but not the redshifts, to a two dimensional surface by using a process of competitive learning where neurons compete to more closely resemble the training data multidimensional space... (read more)

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