A method of searching for supernova candidates from massive galaxy spectra

This paper presents a novel spectroscopic method for searching for supernova candidates from massive galaxy spectra, which is expected to be applied to the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST). This method includes mainly five steps. The first step is spectral preprocessing, including removing spectral noise using wavelet transform, spectral de-redshift, etc. The second step is decomposition of galactic spectra; we can get the galaxy component and supernova component and calculate the Supernova Statistical Characterization Vector (SNSCV) of each galaxy spectrum. The third step is to decrease samples in all the galaxy spectral datasets according to SNSCV of each spectrum, and to use the LOF (Local Outlier Factor)-based outlier detection algorithm to obtain the preliminary selected spectral data. The fourth step is template matching by cross-correlation, according to the matched results we get the secondary selected spectral data. Finally, we choose the final supernova candidates manually through checking the spectral features characteristic of a supernova. By the spectroscopic method proposed in this paper, thirty-six supernova candidates have been detected in a dataset including 294843 galaxy spectra from the Sloan Digital Sky Survey Data Release 7. Nine of these objects are detected first and the other twenty-seven have been reported in other publications (fifteen of which are detected and reported first by us). The twenty-four new super-nova candidates include twenty la type supernova candidates, three Ic type supernova candidates and one II type supernova candidate.