Photo-$z$ with CuBAN$z$: An improved photometric redshift estimator using Clustering aided Back Propagation Neural network

TL;DR

CuBANz is an improved photometric redshift estimator that combines clustering of training data with neural networks, resulting in higher accuracy and better uncertainty estimates for galaxy redshifts.

Contribution

The paper introduces CuBANz, a novel clustering-based neural network approach for photometric redshift estimation that outperforms existing methods like ANNz.

Findings

Residual error as low as 0.03 for z<0.7

Better uncertainty estimates for redshifts

Outperforms existing neural network codes

Abstract

We present an improved photometric redshift estimator code, CuBAN$z$, that is publicly available at https://goo.gl/fpk90V}{https://goo.gl/fpk90V. It uses the back propagation neural network along with clustering of the training set, which makes it more efficient than existing neural network codes. In CuBAN$z$, 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. Separate neural networks are trained for each cluster using all possible colors, magnitudes and uncertainties in the measurements. For a galaxy with unknown redshift, we identify the closest possible clusters having similar photometric properties and use those clusters to get the photometric redshifts using the particular networks that were trained using those cluster members. For galaxies that do not match with any training cluster, the photometric redshifts are obtained from a separate network that uses entire training set. This clustering method enables us to determine the redshifts more accurately. SDSS Stripe 82 catalog has been used here for the demonstration of the code. For the clustered sources with redshift range $z_{\rm spec}<0.7$, the residual error ($\langle (z_{{\rm spec}}-z_{{\rm phot}})^2\rangle^{1/2} $) in the training/testing phase is as low as 0.03 compared to the existing ANNz code that provides residual error on the same test data set of 0.05. Further, we provide a much better estimate of the uncertainty of the derived photometric redshift.