gs-tec: the Gaia spectrophotometry transient events classifier

Abstract

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 spectrophotometers on board 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 GaiaG magnitude, around 75 per cent of the transients will be robustly classified. The efficiency of the algorithm for Type Ia supernovae (SNe I) is higher than 80 per cent for magnitudes G ≤ 18, dropping to approximately 60 per cent at magnitude G = 19. For SNe II, the efficiency varies from 75 to 60 per cent for G ≤ 18, falling to 50 per cent at G = 19. The purity of our classifier is around 95 per cent for SNe I for all magnitudes. For SNe II, it is over 90 per cent for objects with G ≤ 19. gs-tec also estimates the redshifts with errors of σz ≤ 0.01 and epochs with uncertainties σt ≃ 13 and 32 d for 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.