2020/06/09 by H. Fathivavsari, Hassan Fathivavsari · 7 citations
Chemistry · Physics and Astronomy · #Artificial intelligence #Artificial neural network #Astronomy #Astrophysical Phenomena and Observations #Astrophysics #Astrophysics and Star Formation Studies #Chemistry #Computer science #Emission spectrum #Flux (metallurgy) #Galaxies: Formation, Evolution, Phenomena #Galaxy #Physics #Quasar #Redshift #Reionization #Sky #Spectral line #astro-ph.GA
paper · pdf · doi:10.3847/1538-4357/ab9b7d
published in The Astrophysical Journal 898(2), 114 (IOP Publishing) · Accepted for publication in The Astrophysical Journal (ApJ)
arxiv created 2020/06/09 · openalex created_date 2020/06/19 · openalex publication_date 2020/07/29 · arxiv updated 2020/08/05 · openalex updated_date 2026/08/05
Abstract We have employed a deep neural network, or deep learning , to predict the flux and the shape of the broad Ly α 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 Ly α emission line as the output. We found that our neural-network model predicts quasars’ continua around the Ly α 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 Ly α (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 Ly α spectral region. The model could also be used to study the state of the intergalactic medium during the epoch of reionization.