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A Dynamic, Modular Intelligent-Agent framework for Astronomical Light Curve Analysis and Classification

McWhirter, PR and Wright, S and Steele, IA and Al-Jumeily, D and Hussain, A and Fergus, P (2016) A Dynamic, Modular Intelligent-Agent framework for Astronomical Light Curve Analysis and Classification. In: Intelligent Computing Theories and Application: Lecture Notes in Computer Science , 9771. pp. 820-831. (2016 International Conference on Intelligent Computation, 02 August 2016 - 05 August 2016, Lanzhou,China).

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Abstract

Modern time-domain astronomy is capable of collecting a staggeringly large amount of data on millions of objects in real time. This makes it almost impossible for objects to be identified manually. Therefore the production of methods and systems for the automated classification of time-domain astro-nomical objects is of great importance. The Liverpool Telescope has a number of wide-field image gathering instruments mounted upon its structure. These in-struments have been in operation since March 2009 gathering data of multi-degree sized areas of sky around the current field of view of the main telescope. Utilizing a Structured Query Language database established by a pre-processing operation upon the resultant images, which has identified millions of candidate variable stars with multiple time-varying magnitude observations, we applied a method designed to extract time-translation invariant features from the time-series light curves of each object for future input into a classification system. These efforts were met with limited success due to noise and uneven sampling within the time-series data. Additionally, finely surveying these light curves is a processing intensive task. Fortunately, these algorithms are capable of multi-threaded implementations based on available resources. Therefore we propose a new system designed to utilize multiple intelligent agents that distribute the data analysis across multiple machines whilst simultaneously a powerful intelligence service operates to constrain the light curves and eliminate false signals due to noise and local alias periods. This system will be highly scalable, capable of operating on a wide range of hardware whilst maintaining the production of ac-curate features based on the fitting of harmonic models to the light curves within the initial Structural Query Language database.

Item Type: Conference or Workshop Item (Paper)
Additional Information: The final publication is available at Springer via http://dx.doi.org/[10.1007/978-3-319-42291-6_81
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QB Astronomy
Divisions: Computer Science
Astrophysics Research Institute
Publisher: Springer Verlag (Germany)
Date Deposited: 16 Dec 2016 12:27
Last Modified: 20 Mar 2017 14:17
URI: http://researchonline.ljmu.ac.uk/id/eprint/3547

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