Classification of Broad Absorption Line Quasars with a Convolutional Neural Network. (arXiv:1901.04506v1 [astro-ph.GA])
<a href="http://arxiv.org/find/astro-ph/1/au:+Guo_Z/0/1/0/all/0/1">Zhiyuan Guo</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Martini_P/0/1/0/all/0/1">Paul Martini</a>
Quasars that exhibit blue-shifted, broad absorption lines (BAL QSOs) are an
important probe of black hole feedback on galaxy evolution. Yet the presence of
BALs is also a complication for large, spectroscopic surveys that use quasars
as cosmological probes because the BAL features can affect redshift
measurements and contaminate information about the matter distribution in the
Lyman-$alpha$ forest. We present a new BAL QSO catalog for quasars in the
Sloan Digital Sky Survey (SDSS) Data Release 14 (DR14). As the SDSS DR14 quasar
catalog has over 500,000 quasars, we have developed an automated BAL classifier
with a Convolutional Neural Network (CNN). We trained our CNN classifier on the
C IV $lambda 1549$ region of a sample of quasars with reliable human
classifications, and compared the results to both a dedicated test sample and
visual classifications from the earlier SDSS DR12 quasar catalog. Our CNN
classifier correctly classifies over 98% of the BAL quasars in the DR12
catalog, which demonstrates comparable reliability to human classification. The
disagreements are generally for quasars with lower signal-to-noise ratio
spectra and/or weaker BAL features. Our new catalog includes the probability
that each quasar is a BAL, the strength, blueshifts and velocity widths of the
troughs, and similar information for any Si IV $lambda 1398$ BAL troughs that
may be present. We find significant BAL features in 16.8% of all quasars with
$1.57 < z < 5.56$ in the SDSS DR14 quasar catalog.
Quasars that exhibit blue-shifted, broad absorption lines (BAL QSOs) are an
important probe of black hole feedback on galaxy evolution. Yet the presence of
BALs is also a complication for large, spectroscopic surveys that use quasars
as cosmological probes because the BAL features can affect redshift
measurements and contaminate information about the matter distribution in the
Lyman-$alpha$ forest. We present a new BAL QSO catalog for quasars in the
Sloan Digital Sky Survey (SDSS) Data Release 14 (DR14). As the SDSS DR14 quasar
catalog has over 500,000 quasars, we have developed an automated BAL classifier
with a Convolutional Neural Network (CNN). We trained our CNN classifier on the
C IV $lambda 1549$ region of a sample of quasars with reliable human
classifications, and compared the results to both a dedicated test sample and
visual classifications from the earlier SDSS DR12 quasar catalog. Our CNN
classifier correctly classifies over 98% of the BAL quasars in the DR12
catalog, which demonstrates comparable reliability to human classification. The
disagreements are generally for quasars with lower signal-to-noise ratio
spectra and/or weaker BAL features. Our new catalog includes the probability
that each quasar is a BAL, the strength, blueshifts and velocity widths of the
troughs, and similar information for any Si IV $lambda 1398$ BAL troughs that
may be present. We find significant BAL features in 16.8% of all quasars with
$1.57 < z < 5.56$ in the SDSS DR14 quasar catalog.
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