Abstract :
The continuous development of ground-based and space-born sky surveys from gamma-rays to radio (Multi-Wavelength-Surveys) aligns astrophysics with the big data era. The amount of data generated by sky surveys will excess that of peta or even exabytes. This tsunami of data in astrophysics requires new methodologies for the analysis and interpretation since traditional methods cannot deal with such amounts of data.
In this talk, I will give a brief description of the on-going surveys and present how machine learning algorithms such as the classification tree, the linear discriminant analysis and the K-nearest neighbor can be used to identify stellar objects such as planetary nebulae, symbiotic stars or cataclysmic variables.
