Deep Learning Prediction of Quasars Broad Ly$\alpha$ Emission Line

Abstract

We have employed deep neural network, or deep learning to predict the flux and the shape of the broad Ly$\alpha$ emission lines in the spectra of quasars. We use 17870 high signal-to-noise ratio (SNR > 15) quasar spectra from the Sloan Digital Sky Survey (SDSS) Data Release 14 (DR14) to train the model and evaluate its performance. The SiIV, CIV, and CIII] broad emission lines are used as the input to the neural network, and the model returns the predicted Ly$\alpha$ emission line as the output. We found that our neural network model predicts quasars continua around the Ly$\alpha$ spectral region with $\sim$6 - 12% precision and $\lesssim$1% bias. Our model can be used to estimate the HI column density of eclipsing and ghostly damped Ly$\alpha$ (DLA) absorbers as the presence of the DLA absorption in these systems strongly contaminates the flux and the shape of the quasar continuum around Ly$\alpha$ spectral region. The model could also be used to study the state of the intergalactic medium during the epoch of reionization.

Accepted for publication in The Astrophysical Journal (ApJ)

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• IGM damping wing constraints on reionisation from covariance reconstruction of two $z\gtrsim 7$ QSOs