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Time series data mining is an important field of research in the era of "BigData". Next generation astronomical surveys will generate data at unprecedentedrates, creating the need for automated methods of data analysis. We propose amethod of light curve characterisation that employs a pipeline consisting of aneural network with a Long-Short Term Memory Variational Autoencoderarchitecture and a Gaussian mixture model. The pipeline performs extraction andaggregation of features from light curve segments into feature vectors of fixedlength which we refer to as light curve "fingerprints". This representation canbe readily used as input of down-stream machine learning algorithms. Wedemonstrate the proposed method on a data set of Rossi X-ray Timing Explorerobservations of the galactic black hole X-ray binary GRS 1915+105, which waschosen because of its observed complex X-ray variability. We find that theproposed method can generate a representation that characterises theobservations and reflects the presence of distinct classes of GRS 1915+105X-ray flux variability. We find that this representation can be used to performefficient classification of light curves. We also present how therepresentation can be used to quantify the similarity of different lightcurves, highlighting the problem of the popular classification system of GRS1915+105 observations, which does not account for intermediate class behaviour.

monthly notices of the royal astronomical society

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