Data-mining massive time series astronomical data: challenges, problems and solutions

TLDR: The major characteristics of the time series astronomical data, data preprocessing techniques to process these time series, and some domain-specific techniques to separate candidate variable stars from the nonvariant ones are presented.

Abstract: In this paper we present some initial results of a project which uses data-mining techniques to search for evidence of massive compact halo objects (MACHOs) from very large time series database. MACHOs are the proposed materials that probably make the “dark matter” surrounding our own and other galaxies. It was suggested that MACHOs may be detected through the gravitational microlensing effect which can be identified from the light curves of background stars. The objective of this project is two-fold, namely, (i) identification of new classes of variable stars and (ii) detection of microlensing events. In this paper, we present the major characteristics of the time series astronomical data, data preprocessing techniques to process these time series, and some domain-specific techniques to separate candidate variable stars from the nonvariant ones. We discuss the use of the Fourier model to represent the time series and the k -means based clustering method to classify variable stars.