The Large Synoptic Survey Telescope, or LSST, is an ambitious survey whose objective is to provide an unprecedented volume of images and information, using the biggest ground camera of its kind and being capable of fully scanning the night sky each four nights. This thesis will present several contributions to the software developed for the LSST telescope with the purpose of contributing to the detection of type Ia supernovae. We will present the different contributions, based on the existing LSST code and algorithms, in order to create a type Ia supernovae detection dedicated pipeline. It will be capable of finding these specific types of transients with high accuracy and select them automatically through machine learning techniques.
Our results allowed us to identify with high precision and recall type Ia supernovae among the candidates for a simulated test set and a real set.
These results are encouraging and open new path for research in the future.
Members of committee:
Co-directeur de thèse : Marcela Hernández Hoyos, Universidad de los Andes
Co-directeur de thèse : Dominique Fouchez, Aix-Marseille Université
Rapporteur externe : Jaime Forero-Romero, Universidad de los Andes
Rapporteur externe : Philippe GRIS, Université Blaise Pascal
Jury : Anne Ealet, Université de Lyon
Jury : Cristinel Diaconu, Université d’Aix-Marseille
Jury : José Tiberio Hernández, Universidad de los Andes