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Paper   IPM / Astronomy / 16382
School of Astronomy
  Title:   Using Machine Learning to Find Ghostly Damped Lyα Systems in SDSS DR14
  Author(s):  Hassan . Fathivavsari
  Status:   Published
  Journal: Astrophysical Journal
  Vol.:  901
  Year:  2020
  Supported by:            ipm IPM
  Abstract:
We report the discovery of 59 new ghostly absorbers from the Sloan Digital Sky Survey (SDSS) Data Release 14 (DR14). These absorbers, with zabs∼zQSO, reveal no Lyα absorption, and they are mainly identified through the detection of strong metal absorption lines in the spectra. The number of previously known such systems is 30. The new systems are found with the aid of machine learning algorithms. The spectra of 41 (out of total of 89) absorbers also cover the Lyβ spectral region. By fitting the damping wings of the Lyβ absorption in the stacked spectrum of 21 (out of 41) absorbers with relatively stronger Lyβ absorption, we measured an HI column density of logN(HI)=21.50. This column density is 0.5dex higher than that of the previous work. We also found that the metal absorption lines in the stacked spectrum of the 21 ghostly absorbers with stronger Lyβ absorption have similar properties as those in the stacked spectrum of the remaining systems. These circumstantial evidence strongly suggest that the majority of our ghostly absorbers are indeed DLAs

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