Photometric redshift estimation using Gaussian processes

TL;DR

This paper compares Gaussian processes and artificial neural networks for estimating galaxy redshifts from photometric data, highlighting GPs' robustness to training set degradation and their potential advantages in high-redshift surveys.

Contribution

It demonstrates that Gaussian processes outperform neural networks in interpolating across redshift gaps and maintain smoother performance degradation with smaller training sets.

Findings

GPs perform similarly to ANNs with large training sets

GPs interpolate well across redshift gaps where ANNs fail

GPs show smoother error increase with reduced training data

Abstract

We present a comparison between Gaussian processes (GPs) and artificial neural networks (ANNs) as methods for determining photometric redshifts for galaxies, given training set data. In particular, we compare their degradation in performance as the training set size is degraded in ways which might be caused by the observational limitations of spectroscopy. We find that performance with large, complete training sets is very similar, although the ANN achieves slightly smaller root mean square errors. If the size of the training set is reduced by random sampling, the RMS errors of both methods increase, but they do so to a lesser extent and in a much smoother manner for the case of GP regression. When training objects are removed at redshifts 1.3<z<1.7, to simulate the effects of the "redshift desert" of optical spectroscopy, the GP regression is successful at interpolating across the redshift gap, while the ANN suffers from strong bias for test objects in this redshift range. Overall, GP regression has attractive properties for photometric redshift estimation, particularly for deep, high-redshift surveys where it is difficult to obtain a large, complete training set. At present, unlike the ANN code, public GP regression codes do not take account of inhomogeneous measurement errors on the photometric data, and thus cannot estimate reliable uncertainties on the predicted redshifts. However, a better treatment of errors is in principle possible, and the promising results in this paper suggest that such improved GP algorithms should be pursued. (abridged)