• Corpus ID: 52133926

# 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}
}
• Published 29 August 2018
• Physics
• ArXiv
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…

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