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- W2783014526 abstract "The goal of the presented work is the application of data-driven methods on complex and high-dimensional astronomical databases. The focus of the work is the exploration of novel datarepresentations in order to enable the use of statistical learning approaches in the analysis ofdata. With the help of diverse science cases, the advantages of the introduced approaches forclassication, visualization and regression tasks are shown by applying the developed methodologyto astronomical data.In the first part, an alternative approach for estimating redshifts of spectra by using theknowledge about the redshifts provided by the SDSS pipeline is presented. A novel data repre-sentation is employed which contains only information relevant for estimating the redshift andthe detection of multiple redshift systems. Subsequently, a novel data representation for regu-larly sampled light curves based on recurrent networks is presented. This allows an explorativeinvestigation of huge databases with unlabeled data. Finally, a new way of representing the staticpart of irregularly sampled light curves by a mixture of Gaussians is discussed. This represen-tation is more general than the extraction of features, as it allows the inclusion of photometricuncertainties and avoids the introduction of observational biases." @default.
- W2783014526 created "2018-01-26" @default.
- W2783014526 creator A5062154990 @default.
- W2783014526 date "2015-01-01" @default.
- W2783014526 modified "2023-09-23" @default.
- W2783014526 title "On the application ofmachine learning approaches in astronomy:Exploring novel representations ofhigh-dimensional and complex astronomical data" @default.
- W2783014526 doi "https://doi.org/10.11588/heidok.00019927" @default.
- W2783014526 hasPublicationYear "2015" @default.
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