Identifying MgII Narrow Absorption Lines with Deep Learning
  Metal absorption line systems in distant quasar spectra probe of the history
of gas content in the universe. The MgII $\lambda \lambda$ 2796, 2803 doublet
is one of the most important absorption lines since it is a proxy of the star
formation rate and a tracer of the cold gas associated with high redshift
galaxies. Machine learning algorithms have been used to detect absorption lines
systems in large sky surveys, such as Principle Component Analysis (PCA),
Gaussian Process (GP) and decision trees. A very powerful algorithm in the
field of machine learning called deep neural networks, or '' deep learning'' is
a new structure of neural network that automatically extracts semantic features
from raw data and represents them at a high level. In this paper, we apply a
deep convolutional neural network for absorption line detection. We use the
previously published DR7 MgII catalog (Zhu et al. 2013) as the training and
validation sample and the DR12 MgII catalog as the test set. Our deep learning
algorithm is capable of detecting MgII absorption lines with an accuracy of
$\sim$94% . It takes only $\sim 9$ seconds to analyze $\sim$ 50000 quasar
spectra with our deep neural network, which is ten thousand times faster than
traditional methods, while preserving high accuracy with little human
interference. Our study shows that Mg II absorption line detection accuracy of
a deep neutral network model strongly depends on the filter size in the filter
layer of the neural network, and the best results are obtained when the filter
size closely matches the absorption feature size.
