GS-TEC: the Gaia Spectrophotometry Transient Events Classifier

TL;DR

GS-TEC is a Bayesian algorithm that classifies transient objects detected by Gaia using low-resolution spectra, achieving high accuracy and redshift estimation, especially for brighter objects, and can be adapted for other surveys.

Contribution

The paper introduces GS-TEC, a novel Bayesian classifier for Gaia transient spectra, with improved efficiency and purity, and capable of estimating redshifts and epochs.

Findings

75% classification accuracy for transients brighter than G=19

Over 80% efficiency for SNe type I at G≤18

Redshift errors of σ_z ≤ 0.01 for classified transients

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 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.