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[ascl:1609.010] CuBANz: Photometric redshift estimator

CuBANz is a photometric redshift estimator code for high redshift galaxies that uses the back propagation neural network along with clustering of the training set, making it very efficient. The training set is divided into several self learning clusters with galaxies having similar photometric properties and spectroscopic redshifts within a given span. The clustering algorithm uses the color information (i.e. u-g, g-r etc.) rather than the apparent magnitudes at various photometric bands, as the photometric redshift is more sensitive to the flux differences between different bands rather than the actual values. The clustering method enables accurate determination of the redshifts. CuBANz considers uncertainty in the photometric measurements as well as uncertainty in the neural network training. The code is written in C.

Code site:
https://sites.google.com/site/sscubanz/ https://drive.google.com/file/d/0B7h5V5dsldnvdFFDZmNlOWZKQWM/view
Described in:
http://adsabs.harvard.edu/abs/2017NewA...51..169S
Bibcode:
2016ascl.soft09010S

Views: 1583

ascl:1609.010
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