Gravitational waves represent a new opportunity to study and interpret phenomena from the universe. In order to efficiently detect and analyze them, advanced and automatic signal processing and machine learning techniques could help to support standard tools and techniques. Another challenge relates to the large volume of data collected by the detectors on a daily basis, which creates a gap between the amount of data generated and effectively analyzed. In this paper, we propose two approaches involving deep auto-encoder models to analyze time series collected from Gravitational Waves detectors and provide a classification label (noise or real signal). The purpose is to discard noisy time series accurately and identify time series that potentially contain a real phenomenon. Experiments carried out on three datasets show that the proposed approaches implemented using the Apache Spark framework, represent a valuable machine learning tool for astrophysical analysis, offering competitive accuracy and scalability performances with respect to state-of-the-art methods.

引力波代表了研究和解释宇宙现象的新机会。为了有效地检测和分析它们，先进的自动信号处理和机器学习技术可以帮助支持标准工具和技术。另一个挑战涉及检测器每天收集的大量数据，这在生成和有效分析的数据量之间造成了差距。在本文中，我们提出了两种涉及深度自动编码器模型的方法，以分析从引力波探测器收集的时间序列并提供分类标签（噪声或真实信号）。目的是准确丢弃嘈杂的时间序列，并确定可能包含真实现象的时间序列。