Spectroscopic redshift determination with Bayesian convolutional networks

Abstract

Astronomy is facing large amounts of data, so astronomers have to rely on automated methods to analyse them. However, automated methods might produce incorrect values. Therefore, we need to develop different automated methods and perform a consistency check to identify them. If there is a lot of labelled data, convolutional neural networks are a powerful method for any task. We illustrate the consistency check on spectroscopic redshift determination with a method based on a Bayesian convolutional neural network inspired by VGG networks. The method provides predictive uncertainties that enable us to (1.) determine unusual or problematic spectra for visual inspection; (2.) do thresholding that allows us to balance between the error of redshift predictions and coverage. We used the 12th Sloan Digital Sky Survey quasar superset as the training set for the method. We evaluated its generalisation capability on about three-quarters of a million spectra from the 16th quasar superset of the same survey. On the 16th quasar superset, the method performs better in terms of the root-mean-squared error than the most used template fitting method. Using redshift predictions of the proposed method, we identified spectra with incorrectly determined redshifts that are unrecognised quasars or were misclassified as them.