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.
Highlights
The study of transient phenomena is a field of increasing interest: for example, the observations of type Ia Supernovae (SNe) have lead to the discovery of the accelerated expansion of the Universe (Perlmutter et al (1999), Riess et al (1998)) and have played a fundamental role in the discovery of Dark Energy
This paper describes the classification algorithm developed to enable the prototyping of SNe events from Gaia, where the primary information source is the Gaia low resolution spectrophotometric data
As PESSTO transients were selected from a realistic survey we did not use the weights in the purity calculation, as the ratio between objects belonging to different classes is already implicit in the test sample
Summary
The study of transient phenomena is a field of increasing interest: for example, the observations of type Ia Supernovae (SNe) have lead to the discovery of the accelerated expansion of the Universe (Perlmutter et al (1999), Riess et al (1998)) and have played a fundamental role in the discovery of Dark Energy. The investigation of transient phenomena at multiple wavelengths have lead to a better understanding of SNe progenitors (Smartt 2009) and modelling of the explosion mechanisms. Will provide highly accurate parallaxes for over a billion stars. It will provide a wealth of additional information about each star: full six dimensional astrometric parameters; and astrophysical parameters such as effective temperature, surface gravity, metallicties and reddening. Since Gaia will observe each point of the sky around 70 times on average, it will, over the
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