Catarina S. Alves
University College London

Abstract
In 2022, the Legacy Survey of Space and Time (LSST) will start, repeatedly surveying the sky in 6 photometric bands and observing ca. 10 million time-domain events per night. Due to the data volume, the classification and analysis of the events needs to be performed in an automated manner as it is impossible to visually inspect all of them. In preparation for LSST, an open data challenge to classify simulated astronomical sources that vary with time into different classes was hosted in 2018: the Photometric LSST Astronomical Time-Series Classification Challenge (PLAsTiCC). The time-series data provided were simulations of what we expect from the upcoming LSST, incorporating realistic observing conditions.
In this project we use machine learning to classify the light curves of SN Ia, SN Ibc and SN II in PLAsTiCC data. In order to do that, we use snmachine, a flexible and modular library to classify different types of SN developed for Lochner et al. (2016). In particular, we will extract features from the light curves by performing a wavelet decomposition.

2020 January 15, 15:00

IA/U.Porto
Centro de Astrofísica da Universidade do Porto (Classroom)
Rua das Estrelas, 4150-762 Porto