Stellar Karaoke: deep blind separation of terrestrial atmospheric effects out of stellar spectra by velocity whitening.

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 versus 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 interobservation 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 and with qualitative and quantitative evaluations against a data base 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 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. [ABSTRACT FROM AUTHOR]