Photometric Redshift Estimation with a Convolutional Neural Network: NetZ

TL;DR

This paper introduces NetZ, a CNN-based method that estimates galaxy photometric redshifts from images, outperforming previous methods especially at high redshifts, and provides over 34 million new photo-z values for wide application.

Contribution

NetZ is a novel CNN approach that predicts galaxy redshifts directly from images, improving accuracy across a broad redshift range compared to traditional photometry-based methods.

Findings

Achieves a redshift prediction accuracy of σ=0.12 over 0-4 range.

Performs better at high redshifts than previous methods.

Provides over 34 million new photo-z estimates for galaxy surveys.

Abstract

The redshifts of galaxies are a key attribute that is needed for nearly all extragalactic studies. Since spectroscopic redshifts require additional telescope and human resources, millions of galaxies are known without spectroscopic redshifts. Therefore, it is crucial to have methods for estimating the redshift of a galaxy based on its photometric properties, the so-called photo-$z$. We developed NetZ, a new method using a Convolutional Neural Network (CNN) to predict the photo-$z$ based on galaxy images, in contrast to previous methods which often used only the integrated photometry of galaxies without their images. We use data from the Hyper Suprime-Cam Subaru Strategic Program (HSC SSP) in five different filters as training data. The network over the whole redshift range between 0 and 4 performs well overall and especially in the high-$z$ range better than other methods on the same data. We obtain an accuracy $|z_\text{pred}-z_\text{ref}|$ of $\sigma = 0.12$ (68% confidence interval) with a CNN working for all galaxy types averaged over all galaxies in the redshift range of 0 to $\sim$4. By limiting to smaller redshift ranges or to Luminous Red Galaxies (LRGs), we find a further notable improvement. We publish more than 34 million new photo-$z$ values predicted with NetZ here. This shows that the new method is very simple and fast to apply, and, importantly, covers a wide redshift range limited only by the available training data. It is broadly applicable and beneficial to imaging surveys, particularly upcoming surveys like the Rubin Observatory Legacy Survey of Space and Time which will provide images of billions of galaxies with similar image quality as HSC.