GS-TEC: the Gaia Spectrophotometry Transient Events Classifier
  We present an algorithm for classifying the nearby transient objects detected by the Gaia satellite. The algorithm will use the low-resolution spectra from the blue and red spectro-photometers on board of the satellite. Taking a Bayesian approach we model the spectra using the newly constructed reference spectral library and literature-driven priors. We find that for magnitudes brighter than 19 in Gaia $G$ magnitude, around 75\% of the transients will be robustly classified. The efficiency of the algorithm for SNe type I is higher than 80\% for magnitudes $G\leq$18, dropping to approximately 60\% at magnitude $G$=19. For SNe type II, the efficiency varies from 75 to 60\% for $G\leq$18, falling to 50\% at $G$=19. The purity of our classifier is around 95\% for SNe type I for all magnitudes. For SNe type II it is over 90\% for objects with $G \leq$19. GS-TEC also estimates the redshifts with errors of $\sigma_z \le$ 0.01 and epochs with uncertainties $\sigma_t \simeq$ 13 and 32 days for type SNe I and SNe II respectively. GS-TEC has been designed to be used on partially calibrated Gaia data. However, the concept could be extended to other kinds of low resolution spectra classification for ongoing surveys. 
Nadejda Blagorodnova and Sergey E. Koposov and {\L}ukasz Wyrzykowski   and Mike Irwin and Nicholas A. Walton
astro-ph.IM astro-ph.CO