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

Light-curve fingerprints: An automated approach to the extraction of X-ray variability patterns with feature aggregation - An example application to GRS 1915+105
Light-curve fingerprints: An automated approach to the extraction of X-ray variability patterns with feature aggregation - An example application to GRS 1915+105
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 characterization 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 that 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 characterizes 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.
0035-8711
Orwat-Kapola, Jakub, Kacper
6c9db0de-ab29-414b-9e08-44c43e658f7d
Bird, Antony
045ee141-4720-46fd-a412-5aa848a91b32
Hill, Adam
b1007941-b5b1-47cd-8476-7c6b9c57f347
Altamirano, Diego
d5ccdb09-0b71-4303-9538-05b467be075b
Huppenkothen, Daniela
f03b1596-7c31-4b18-a869-13660a7b60c2
Orwat-Kapola, Jakub, Kacper
6c9db0de-ab29-414b-9e08-44c43e658f7d
Bird, Antony
045ee141-4720-46fd-a412-5aa848a91b32
Hill, Adam
b1007941-b5b1-47cd-8476-7c6b9c57f347
Altamirano, Diego
d5ccdb09-0b71-4303-9538-05b467be075b
Huppenkothen, Daniela
f03b1596-7c31-4b18-a869-13660a7b60c2

Orwat-Kapola, Jakub, Kacper, Bird, Antony, Hill, Adam, Altamirano, Diego and Huppenkothen, Daniela (2021) Light-curve fingerprints: An automated approach to the extraction of X-ray variability patterns with feature aggregation - An example application to GRS 1915+105. Monthly Notices of the Royal Astronomical Society.

Record type: Article

Abstract

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 characterization 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 that 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 characterizes 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.

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2110.10063 - Accepted Manuscript
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Accepted/In Press date: 8 October 2021
Published date: 23 October 2021
Additional Information: arXiv:2110.10063

Identifiers

Local EPrints ID: 454224
URI: http://eprints.soton.ac.uk/id/eprint/454224
ISSN: 0035-8711
PURE UUID: 1f07c74a-e97e-4328-a122-f6d3605dac52
ORCID for Jakub, Kacper Orwat-Kapola: ORCID iD orcid.org/0000-0002-3984-1881
ORCID for Antony Bird: ORCID iD orcid.org/0000-0002-6888-8937
ORCID for Adam Hill: ORCID iD orcid.org/0000-0003-3470-4834
ORCID for Diego Altamirano: ORCID iD orcid.org/0000-0002-3422-0074

Catalogue record

Date deposited: 02 Feb 2022 17:57
Last modified: 03 Feb 2022 02:54

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Contributors

Author: Jakub, Kacper Orwat-Kapola ORCID iD
Author: Antony Bird ORCID iD
Author: Adam Hill ORCID iD
Author: Daniela Huppenkothen

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