We introduce QuasarNET, a deep convolutional neural network that performs
classification and redshift estimation of astrophysical spectra with
human-expert accuracy. We pose these two tasks as a \emph{feature detection}
problem: presence or absence of spectral features determines the class, and
their wavelength determines the redshift, very much like human-experts proceed.
When ran on BOSS data to identify quasars through their emission lines,
QuasarNET defines a sample $99.51\pm0.03$\% pure and $99.52\pm0.03$\% complete,
well above the requirements of many analyses using these data. QuasarNET
significantly reduces the problem of line-confusion that induces catastrophic
redshift failures to below 0.2\%. We also extend QuasarNET to classify spectra
with broad absorption line (BAL) features, achieving an accuracy of
$98.0\pm0.4$\% for recognizing BAL and $97.0\pm0.2$\% for rejecting non-BAL
quasars. QuasarNET is trained on data of low signal-to-noise and medium
resolution, typical of current and future astrophysical surveys, and could be
easily applied to classify spectra from current and upcoming surveys such as
eBOSS, DESI and 4MOST.

QuasarNET 是一款深度卷积神经网络，可分类和估算星系物理光谱的红移。该网络解决了光谱学的两个问题：存在或不存在波长决定样本，其频移程度则决定其红移，从而极大地减少了线的混淆和红移失败。