Mining the UKIDDS GPS: Embedded Galactic Clusters and Star Formation (Lauri Haikala, University of Helsinki, Finland)
Several large digital data archives have become publicly available during the last decade (e.g. SDSS, 2MASS and UKIDSS). The catalogues contain hundreds of millions (e.g. SDSS) to thousands of millions (e.g. UKIDSS when finished) objects. Extracting information from a survey containing terabytes of data can naturally be done in the traditional way, case by case, in small restricted areas. But to really optimize the use of all the data, data mining techniques have to be applied. I discuss an application of Gaussian mixture modelling, optimized with the Expectation Maximization algorithm to automatically locate stellar clusters in the UKIDSS GPS. I discuss the practical side and the caveats of mining the UKIDDS database.
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| Cuándo |
11/01/2012 de 01:00 pm a 02:00 pm |
| Dónde | DAA Seminar Room |
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