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Unsupervised clustering visualisation tool for Gaia DR3

Published online by Cambridge University Press:  01 August 2025

Marco Álvarez Open the ORCID record for Marco Álvarez [Opens in a new window] ,
Carlos Dafonte ,
Minia Manteiga Open the ORCID record for Minia Manteiga [Opens in a new window] ,
Daniel Garabato ,
Raúl Santoveña  and
Lara Pallas
Marco Álvarez*
Affiliation:
CITIC - Computer Science and IT, University of A Coruña, Spain
Carlos Dafonte
Affiliation:
CITIC - Computer Science and IT, University of A Coruña, Spain
Minia Manteiga
Affiliation:
CITIC - Nautical Sciences and Marine Engineering, University of A Coruña, Spain
Daniel Garabato
Affiliation:
CITIC - Computer Science and IT, University of A Coruña, Spain
Raúl Santoveña
Affiliation:
CITIC - Computer Science and IT, University of A Coruña, Spain
Lara Pallas
Affiliation:
CITIC - Computer Science and IT, University of A Coruña, Spain
*
email: marco.antonio.agonzalez@udc.es
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Abstract

The Gaia mission DR3 provides accurate data of around two billion stars in the Galaxy, including a classification based on astronomical classes of objects. In this work we present a web visualization tool to analyze one of the products published in the DR3, the Outlier Analysis Self-Organizing Map†.

Keywords

GaiaDR3Self-Organizing MapsVisualizationBig Data

Information

Type
Poster Paper
Information
Proceedings of the International Astronomical Union , Volume 19 , Symposium S368: Machine Learning in Astronomy: Possibilities and Pitfalls , August 2023 , pp. 98 - 100
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Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of International Astronomical Union

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Footnotes

†

Data available at https://cdn.gea.esac.esa.int/Gaia/gdr3/

References

Gaia Collaboration, Vallenari, A., et al., 2022, Gaia Data Release 3. Summary of the content and survey properties, Astronomy & AstrophysicsGoogle Scholar
Delchambre, et al., 2022, Gaia Data Release 3: Apsis III - Non-stellar content and source classification, Astronomy & AstrophysicsGoogle Scholar
Dafonte, C., Garabato, D., Álvarez, M. and Manteiga, M., 2018, Distributed Fast Self-Organized Maps for Massive Spectrophotometric Data Analysis. Sensors 18 (5), pp. 1419 CrossRefGoogle ScholarPubMed
Álvarez, M. A., Dafonte, C., Manteiga, M., Garabato, D. and Santoveña, R., 2021, GUASOM: an adaptive visualization tool forunsupervised clustering in spectrophotometric astronomical surveys, Neural Computing and ApplicationsCrossRefGoogle Scholar