Globular cluster detection in the GAIA survey

Mohammad Mohammadi*, Nicolai Petkov, Kerstin Bunte, Reynier Peletier, F.-M. Schleif

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)
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Abstract

Many techniques for the detection of interesting stellar structures in astronomical data sets require full phase-space or color information, which is not always available. The first data release of the GAIA satellite, for example, provided highly accurate positions and magnitudes for more than one billion sources. Therefore the question arises if such structures can also be automatically found without waiting for more detailed information in future data releases. In this contribution we propose and compare two conceptually different strategies to find globular clusters in the GAIA DR1 survey. The first approach is a nearest neighbor retrieval and the second an anomaly detection. Both techniques are able to find most of the known globular clusters within our observation frames consistently, as well as potential candidates for further investigation. Furthermore we address approximation approaches to scale the strategy to larger data.
Original languageEnglish
Pages (from-to)164-171
Number of pages7
JournalNeurocomputing
Volume342
Issue numberSI
DOIs
Publication statusPublished - 21-May-2019

Keywords

  • Astroinformatics
  • Stellar structure analysis
  • Nearest neighbor retrieval
  • Novelty detection

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