Deep Learning Prediction of the Broad Lyα Emission Line of Quasars

Abstract

Abstract We have employed a deep neural network, or deep learning, to predict the flux and the shape of the broad Lyα emission lines in the spectra of quasars. We use 17,870 high signal-to-noise ratio (S/N > 15) quasar spectra from the Sloan Digital Sky Survey Data Release 14 to train the model and evaluate its performance. The Si iv, C iv, and C iii] broad emission lines are used as the input to the neural network, and the model returns the predicted Lyα emission line as the output. We found that our neural-network model predicts quasars’ continua around the Lyα spectral region with ∼6%–12% precision and ≲1% bias. Our model can be used to estimate the H i column density of eclipsing and ghostly damped Lyα (DLA) absorbers, as the presence of the DLA absorption in these systems strongly contaminates the flux and the shape of the quasar continuum around the Lyα spectral region. The model could also be used to study the state of the intergalactic medium during the epoch of reionization.