Mining the UKIDSS GPS: star formation and embedded clusters
O. Solin, E. Ukkonen, and L. Haikala

TL;DR
This study develops a data mining method using Gaussian mixture models and EM algorithm to identify new stellar clusters and star formation sites in the UKIDSS Galactic Plane Survey, successfully discovering 137 clusters and 30 star formation regions.
Contribution
Introduces a novel approach combining mixture models and EM algorithm to detect embedded stellar clusters in large survey data, addressing artefacts and classification issues.
Findings
Identified 137 new stellar clusters in the UKIDSS data.
Detected 30 new sites of embedded star formation.
Successfully filtered out artefacts and false positives.
Abstract
Data mining techniques must be developed and applied to analyse the large public data bases containing hundreds to thousands of millions entries. The aim of this study is to develop methods for locating previously unknown stellar clusters from the UKIDSS Galactic Plane Survey catalogue data. The cluster candidates are computationally searched from pre-filtered catalogue data using a method that fits a mixture model of Gaussian densities and background noise using the Expectation Maximization algorithm. The catalogue data contains a significant number of false sources clustered around bright stars. A large fraction of these artefacts were automatically filtered out before or during the cluster search. The UKIDSS data reduction pipeline tends to classify marginally resolved stellar pairs and objects seen against variable surface brightness as extended objects (or "galaxies" in the archive…
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Taxonomy
TopicsAstronomical Observations and Instrumentation · Astronomy and Astrophysical Research · History and Developments in Astronomy
