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Paper   IPM / Astronomy / 16333
School of Astronomy
  Title:   Deep Learning Prediction of Quasars Broad Lyα Emission Line
  Author(s):  H. Fathivavsari
  Status:   Published
  Journal: ApJ
  Vol.:  898
  Year:  2020
  Supported by:            ipm IPM
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 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 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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