Identifying MgII Narrow Absorption Lines with Deep Learning
Abstract
Metal absorption line systems in distant quasar spectra are probes of the history of the gas content in the universe. The Mg ii λλ 2796, 2803 doublet is one of the most important absorption lines since it is a proxy of the star formation rate and a tracer of the cold gas associated with high-redshift galaxies. Machine learning algorithms have been used to detect absorption lines systems in large sky surveys, such as principal component analysis, Gaussian process, and decision trees. A very powerful algorithm in the field of machine learning called deep neural networks, or ‘deep learning’, is a new structure of neural network that automatically extracts semantic features from raw data and represents them at a high level. In this paper, we apply a deep convolutional neural network for absorption line detection. We use the previously published DR7 Mg ii catalogue as the training and validation sample and the DR12 Mg ii catalogue as the test set. Our deep learning algorithm is capable of detecting Mg ii absorption lines with an accuracy of ∼94 per cent. It takes only ∼9 s to analyse ∼50 000 quasar spectra with our deep neural network, which is ten thousand times faster than traditional methods, while preserving high accuracy with little human interference. Our study shows that Mg ii absorption line detection accuracy of a deep neutral network model strongly depends on the filter size in the filter layer of the neural network, and the best results are obtained when the filter size closely matches the absorption feature size.