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Exploratory Analysis of Light Curves: A Case-Study in Astronomy Data Understanding

机译:光曲线的探索性分析:天文学数据理解的案例研究

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Data acquisition in Biology and Astronomy has seen unprecedented growth in volume since the turn of the century. It will not be an exaggeration to state that the needs of these two sciences are pushing computer science research to new frontiers. The focus of this paper is astronomy, which since inception of Virtual Observatory and commissioning of massive sky surveys is gasping for knowledge in data deluge. Astrocomputing, which subsumes Astroinformatics, is a recent multi-disciplinary field of research with computer science and astronomy at the core. In this article we dwell upon the opportunities and challenges for machine learning and data mining research thrown open by this emerging discipline. We present a case study of an ongoing work on exploratory analysis of unclassified light curves. Though scientific analysis and interpretation of the results of the study are pending, the exercise demonstrates the merit of customized exploratory approach for study. The approach is general and can be applied to light curves obtained from any survey. Owing to the gargantuan scale of astronomy data processing requirements, we discuss scalability of the proposed method.
机译:自世纪之交以来,生物学和天文学的数据采集量就出现了前所未有的增长。毫不夸张地说,这两种科学的需求正在将计算机科学研究推向新的领域。本文的重点是天文学,自虚拟天文台成立以来和进行大规模的天空勘测以来,天文学一直在寻找数据洪水。包含天文信息学的天文计算是近来跨学科的研究领域,以计算机科学和天文学为核心。在本文中,我们详细介绍了这一新兴学科为机器学习和数据挖掘研究带来的机遇和挑战。我们提出了一个正在进行的关于未分类光曲线的探索性分析工作的案例研究。尽管尚待研究结果的科学分析和解释,但该练习证明了定制的探索性研究方法的优点。该方法是通用的,可以应用于从任何勘测获得的光曲线。由于天文学数据处理需求的庞大规模,我们讨论了所提出方法的可扩展性。

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