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.
