Detection of Emission Line Stars from the Gaia Space Telescope
Name
Jürgen Jänes
Abstract
The Gaia probe, set to launch in 2011, will measure an estimated billion astronomical objects during its five-year mission, producing roughly one petabyte of data.
The goal of this thesis is to study the possibilities of using machine learning algorithms for analysing data produced by the low-resolution BP/RP photometer of the space probe. We use simulations of the Gaia observations in our experiments.
The results of the thesis are divided into two parts. First, we look into using support vector machines for solving two different classification tasks. For the first classification task, we try to separate spectra with H-alpha emission from regular stars, obtaining reasonable results. We proceed to further classify Be stars and Wolf-Rayet’ stars.
Second, we try to estimate the equivalent width of the H-alpha spectral line using support vector regression. We introduce a simple, non-physical model for the spectrum of an emission line star. The model applies principal component analysis in order to generate the spectral continuum. Spectral lines are approximated using Gaussian functions. Preliminary results in using support vector regression are encouraging.
Future work includes improving the effectiveness of the classification algorithms, adding additional spectral lines to the spectral model as well as applying support vector regression to other spectral lines.
Graduation Thesis language
English
Graduation Thesis type
Bachelor - Computer Science
Supervisor(s)
Sven Laur, Indrek Kolka
Defence year
2009