Data science methodology for time-delay estimation and data preprocessing of the time-delay challenge

Abstract

The vast amount of astronomical information available over this decade has far exceeded the last century. The heterogeneity of the data and the overwhelming magnitude surpassed the possibilities and capabilities to perform manual analysis. As a consequence, new techniques have been developed and different strategies have also been amalgamated such as data science and data mining in order to carry out a more in-depth and exhaustive analysis in search of extraction of knowledge in data. This paper introduces a data science methodology that consists of successive stages, being the core of this proposal the step of data preprocessing, with the aim to reduce the complexity of the analysis and being able to uncover hidden knowledge in the data. The proposed methodology was tested on a set of data consisting of artificial light curves that try to mimic the behaviour of the strong gravitational lens phenomenon supplied by the Time Delay Challenge 1 (TDC1). Under the data science methodology, diverse statistical methods were implemented for data analysis, and cross-correlation and dispersion methods were applied for time delay estimation of strong lensing systems. With this methodology, we obtained time delay estimations from TDC1 dataset and we compared it with previous results reported by the COSmological MOnitoring of GRAvItational Lenses project (COSMOGRAIL). The empirical evidence leads us to conclude that with the proposed methodology, we achieve greater accuracy in estimating time delays in contrast with estimations made with raw data.