  We report a study exploring how the use of deep neural networks with
astronomical Big Data may help us find and uncover new insights into underlying
phenomena: through our experiments towards unsupervised knowledge extraction
from astronomical Big Data we serendipitously found that deep convolutional
autoencoders tend to reject telluric lines in stellar spectra. With further
experiments we found that only when the spectra are in the barycentric frame
does the network automatically identify the statistical independence between
two components, stellar vs telluric, and rejects the latter. We exploit this
finding and turn it into a proof-of-concept method for removal of the telluric
lines from stellar spectra in a fully unsupervised fashion: we increase the
inter-observation entropy of telluric absorption lines by imposing a random,
virtual radial velocity to the observed spectrum. This technique results in a
non-standard form of ``whitening'' in the atmospheric components of the
spectrum, decorrelating them across multiple observations. We process more than
250,000 spectra from the High Accuracy Radial velocity Planetary Search (HARPS)
and with qualitative and quantitative evaluations against a database of known
telluric lines, show that most of the telluric lines are successfully rejected.
Our approach, `Stellar Karaoke', has zero need for prior knowledge about
parameters such as observation time, location, or the distribution of
atmospheric molecules and processes each spectrum in milliseconds. We also
train and test on Sloan Digital Sky Survey (SDSS) and see a significant
performance drop due to the low resolution. We discuss directions for
developing tools on top of the introduced method in the future.
