Abstract

Classification of spectra (1) and anomaly detection (2) are fundamental steps to guarantee the highest accuracy in redshift measurements (3) in modern all-sky spectroscopic surveys. We introduce a new Galaxy Spectra Neural Network (GaSNet-III) model that utilizes neural networks to perform these three tasks simultaneously with high efficiency. Two different reconstruction networks – an autoencoder-like network and a U-Net – are used to reconstruct the rest-frame spectrum, which is then compared with the observed spectrum via a ?2 metric across the entire type and redshift spaces to find the best-fitting solution. SDSS DR16 spectra are used as a reference data set to provide a fully self-consistent science test to show that our model achieves accuracy comparable to that of classical principal component analysis-based methods, and even better in some specific metrics, while maintaining significantly higher efficiency. In particular, the model achieves an average of > 98 per cent classification accuracy across all classes, and redshift accuracies of over 99 per cent for stars, over 98 per cent for galaxies with errors of the order of O(10?4), and over 93 per cent for quasars with errors of the order of O(10?3). Tests on DESI spectra demonstrate that the model can generalize well to other surveys without retraining, with only a small degradation in performance. Furthermore, by comparing different peaks of ?2 curves, we define a robustness measure that enables the identification of anomalous spectra. The GaSNet-III provides accurate and high-efficiency spectrum modelling to perform accurate redshift estimates and anomaly detection in vast data volumes from future spectroscopic sky surveys. © The Author(s) 2025. Published by Oxford University Press on behalf of Royal Astronomical Society.