• Physics, Computer Science
  • Published in ArXiv 2018

QuasarNET: Human-level spectral classification and redshifting with Deep Neural Networks

@article{Busca2018QuasarNETHS,
  title={QuasarNET: Human-level spectral classification and redshifting with Deep Neural Networks},
  author={Nicolas Busca and Christophe Balland},
  journal={ArXiv},
  year={2018},
  volume={abs/1808.09955}
}
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… CONTINUE READING