  Quasar absorption line analysis is critical for studying gas and dust
components and their physical and chemical properties as well as the evolution
and formation of galaxies in the early universe. Ca II absorbers, which are one
of the dustiest absorbers and are located at lower redshifts than most other
absorbers, are especially valuable when studying physical processes and
conditions in recent galaxies. However, the number of known quasar Ca II
absorbers is relatively low due to the difficulty of detecting them with
traditional methods. In this work, we developed an accurate and quick approach
to search for Ca II absorption lines using deep learning. In our deep learning
model, a convolutional neural network, tuned using simulated data, is used for
the classification task. The simulated training data are generated by inserting
artificial Ca II absorption lines into original quasar spectra from the Sloan
Digital Sky Survey (SDSS) whilst an existing Ca II catalog is adopted as the
test set. The resulting model achieves an accuracy of 96% on the real data in
the test set. Our solution runs thousands of times faster than traditional
methods, taking a fraction of a second to analyze thousands of quasars while
traditional methods may take days to weeks. The trained neural network is
applied to quasar spectra from SDSS's DR7 and DR12 and discovered 399 new
quasar Ca II absorbers. In addition, we confirmed 409 known quasar Ca II
absorbers identified previously by other research groups through traditional
methods.

Astrophysics of Galaxies;Instrumentation and Methods for Astrophysics