Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Using Machine Learning to Find Ghostly Damped Ly$\alpha$ Systems in SDSS
  DR14   We report the discovery of 59 new ghostly absorbers from the Sloan Digital
Sky Survey (SDSS) Data Release 14 (DR14). These absorbers, with $z_{\rm
abs}$$\sim$$z_{\rm QSO}$, reveal no Ly$\alpha$ 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 Ly$\beta$ spectral region. By fitting the
damping wings of the Ly$\beta$ absorption in the stacked spectrum of 21 (out of
41) absorbers with relatively stronger Ly$\beta$ absorption, we measured an HI
column density of log$N$(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 Ly$\beta$ 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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The categories of this paper are: hep-ex
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The categories of this paper are: alg-geom
34
The categories of this paper are: chem-ph
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The categories of this paper are: cs
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The categories of this paper are: hep-th
4
The categories of this paper are: cond-mat
5
The categories of this paper are: gr-qc
37
The categories of this paper are: supr-con
0
The categories of this paper are: hep-ph
21
The categories of this paper are: adap-org
14
The categories of this paper are: math-ph
2
The categories of this paper are: math
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The categories of this paper are: q-bio
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The categories of this paper are: funct-an
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The categories of this paper are: nlin
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The categories of this paper are: bayes-an