“School of Astronomy”
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| Paper IPM / Astronomy / 16333 |
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| Abstract: | |
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We have employed 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 (SNR > 15)
quasar spectra from the Sloan Digital Sky Survey (SDSS) Data Release 14 (DR14) 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
â¼ 6â12 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 Lyα spectral
region. The model could also be used to study the state of the intergalactic medium during the epoch
of reionization
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