GUASOM: An Adaptive Visualization Tool for Unsupervised Clustering in Spectrophotometric Astronomical Surveys
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GUASOM: An Adaptive Visualization Tool for Unsupervised Clustering in Spectrophotometric Astronomical SurveysFecha
2022Cita bibliográfica
Álvarez, M.A., Dafonte, C., Manteiga, M. et al. GUASOM: an adaptive visualization tool for unsupervised clustering in spectrophotometric astronomical surveys. Neural Comput & Applic 34, 1993–2006 (2022). https://doi.org/10.1007/s00521-021-06510-9
Resumen
[Abstract] We present an adaptive visualization tool for unsupervised classification of astronomical objects in a Big Data context such as the one found in the increasingly popular large spectrophotometric sky surveys. This tool is based on an artificial intelligence technique, Kohonen’s self-organizing maps, and our goal is to facilitate the analysis work of the experts by means of oriented domain visualizations, which is impossible to achieve by using a generic tool. We designed a client-server that handles the data treatment and computational tasks to give responses as quickly as possible, and we used JavaScript Object Notation to pack the data between server and client. We optimized, parallelized, and evenly distributed the necessary calculations in a cluster of machines. By applying our clustering tool to several databases, we demonstrated the main advantages of an unsupervised approach: the classification is not based on pre-established models, thus allowing the “natural classes” present in the sample to be discovered, and it is suited to isolate atypical cases, with the important potential for discovery that this entails. Gaia Utility for the Analysis of self-organizing maps is an analysis tool that has been developed in the context of the Data Processing and Analysis Consortium, which processes and analyzes the observations made by ESA’s Gaia satellite (European Space Agency) and prepares the mission archive that is presented to the international community in sequential periodic publications. Our tool is useful not only in the context of the Gaia mission, but also allows segmenting the information present in any other massive spectroscopic or spectrophotometric database.
Palabras clave
Big data astronomy
Self-organizing maps visualization
Computational astrophysics
Artificial neural networks
Astronomical surveys
Self-organizing maps visualization
Computational astrophysics
Artificial neural networks
Astronomical surveys
Descripción
Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG
Versión del editor
Derechos
Atribución 4.0 Internacional
ISSN
1433-3058