Machine learning applied to classification methods supernovae.

Name: Rodrigo Duarte Silva
Type: MSc dissertation
Publication date: 06/09/2018
Advisor:

Name Rolesort descending
Valerio Marra Advisor *
Luciano Casarini Co-advisor *

Examining board:

Name Rolesort descending
Valerio Marra Advisor *
Luciano Casarini Co advisor *
Ribamar Rondon de Rezende dos Reis External Examiner *
Júlio César Fabris Internal Examiner *

Summary: Future observational research with investments, telescopes and technologies never before seen, are being proposed in an attempt to unravel the mysteries of the Universe. In our work, we provide an overview of this scenario, with special attention to the classification
of supernovae that will be done by LSST (Large Synoptic Survey Telescope) from 2022.
Initially, we introduce the physics that involve the Supernova event and its observation, with the objective of treating the problem of photometric classification of supernovae. We provide important references in the use of different machine learning and neural networks
for this purpose. We include results from the use of some of the computational methods and the theory behind them, highlighting their potentialities and vunerabilities.
Machine learning methods may involve supervision or not. We aim to describe the application of these powerful tools in the analysis of observational data and verify unexpected results.

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