title: On the application of  machine learning approaches in astronomy:  Exploring novel representations of  high-dimensional and complex astronomical data
creator: Kügler, Sven Dennis
subject: ddc-004
subject: 004 Data processing Computer science
subject: ddc-520
subject: 520 Astronomy and allied sciences
description: 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 data  representations in order to enable the use of statistical learning approaches in the analysis of  data. With the help of diverse science cases, the advantages of the introduced approaches for  classication, visualization and regression tasks are shown by applying the developed methodology  to astronomical data.  In the first part, an alternative approach for estimating redshifts of spectra by using the  knowledge 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 and  the 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 explorative  investigation of huge databases with unlabeled data. Finally, a new way of representing the static  part 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 photometric  uncertainties and avoids the introduction of observational biases.  
date: 2015
type: Dissertation
type: info:eu-repo/semantics/doctoralThesis
type: NonPeerReviewed
format: application/pdf
identifier: https://archiv.ub.uni-heidelberg.de/volltextserverhttps://archiv.ub.uni-heidelberg.de/volltextserver/19927/1/thesis.pdf
identifier: DOI:10.11588/heidok.00019927
identifier: urn:nbn:de:bsz:16-heidok-199273
identifier:   Kügler, Sven Dennis  (2015) On the application of machine learning approaches in astronomy: Exploring novel representations of high-dimensional and complex astronomical data.  [Dissertation]     
relation: https://archiv.ub.uni-heidelberg.de/volltextserver/19927/
rights: info:eu-repo/semantics/openAccess
rights: http://archiv.ub.uni-heidelberg.de/volltextserver/help/license_urhg.html
language: eng