标题： 光变曲线指纹：基于特征聚合的X射线变率模式自动提取方法——以GRS 1915+105为例的应用 显示英文标题

标题： Light curve fingerprints: an automated approach to the extraction of X-ray variability patterns with feature aggregation -- an example application to GRS 1915+105

摘要： 在“大数据”时代，时间序列数据挖掘是一个重要的研究领域。 下一代天文巡天观测将以前所未有的速度生成数据，从而需要自动化数据分析方法。 我们提出了一种用于光变曲线特征提取的方法，该方法采用了一个管道模型，其中包括具有长短时记忆变分自编码器结构的神经网络和高斯混合模型。 该管道从光变曲线片段中提取并聚合特征到固定长度的特征向量中，我们将其称为光变曲线的“指纹”。 这种表示可以作为下游机器学习算法的直接输入。 我们在Rossi X射线定时探测器对银河系黑洞X射线双星GRS 1915+105的观测数据集上验证了所提出的方法，选择这个数据集是因为它表现出复杂的X射线变化。 我们发现所提出的方法能够生成一种表征观测值并反映GRS 1915+105不同X射线流量变化类别的表示。 我们还发现这种表示可用于高效地分类光变曲线。 此外，我们展示了如何使用这种表示来量化不同光变曲线之间的相似性，强调了GRS 1915+105观测流行的分类系统的问题，因为它没有考虑到中间类行为。 显示英文摘要

摘要： Time series data mining is an important field of research in the era of "Big Data". Next generation astronomical surveys will generate data at unprecedented rates, creating the need for automated methods of data analysis. We propose a method of light curve characterisation that employs a pipeline consisting of a neural network with a Long-Short Term Memory Variational Autoencoder architecture and a Gaussian mixture model. The pipeline performs extraction and aggregation of features from light curve segments into feature vectors of fixed length which we refer to as light curve "fingerprints". This representation can be readily used as input of down-stream machine learning algorithms. We demonstrate the proposed method on a data set of Rossi X-ray Timing Explorer observations of the galactic black hole X-ray binary GRS 1915+105, which was chosen because of its observed complex X-ray variability. We find that the proposed method can generate a representation that characterises the observations and reflects the presence of distinct classes of GRS 1915+105 X-ray flux variability. We find that this representation can be used to perform efficient classification of light curves. We also present how the representation can be used to quantify the similarity of different light curves, highlighting the problem of the popular classification system of GRS 1915+105 observations, which does not account for intermediate class behaviour.