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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 Lyñ 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 Lyñ emission line as the output. We
found that our neural network model predicts quasars continua around the Lyñ spectral region with
â?¼ 6â??12 % precision and . 1% bias. Our model can be used to estimate the H i column density of
eclipsing and ghostly damped Lyñ (DLA) absorbers as the presence of the DLA absorption in these
systems strongly contaminates the flux and the shape of the quasar continuum around Lyñ 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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