Probabilistic photometric redshift estimation in massive digital sky surveys via machine learning

• Verfasst von: D'Isanto, Antonio [VerfasserIn]
• Titel: Probabilistic photometric redshift estimation in massive digital sky surveys via machine learning
• Mitwirkende: Wambsganß, Joachim [AkademischeR BetreuerIn]
• Verf.angabe: Put forward by Antonio D'Isanto ; referees: Prof. Dr. Joachim Wambsganß, Dr. Coryn Bailer-Jones
• Verlagsort: Heidelberg
• Jahr: 2019
• Umfang: 1 Online-Ressource (108 Seiten)
• Illustrationen: Illustrationen, Diagramme
• Schrift/Sprache: Mit einer Zusammenfassung in deutscher und englischer Sprache
• Hochschulschrift: Dissertation, Ruperto-Carola-University of Heidelberg, 2019
• DOI: doi:10.11588/heidok.00026000
• URN: urn:nbn:de:bsz:16-heidok-260000
• Datenträger: Online-Ressource
• Dokumenttyp: Hochschulschrift
• Sprache: eng
• Bibliogr. Hinweis: Erscheint auch als : Druck-Ausgabe: D'Isanto, Antonio, 1985 - : Probabilistic photometric redshift estimation in massive digital sky surveys via machine learning. - Heidelberg, 2019. - 108 Seiten
• K10plus-PPN: 1656013843
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext

Abstract

Abstract: The problem of photometric redshift estimation is a major subject in astronomy, since the need of estimating distances for a huge number of sources, as required by the data deluge of the recent years. The ability to estimate redshifts through spectroscopy does not scale with this avalanche of data. Photometric redshifts provide the required redshift estimates at the cost of some precision. The success of several forthcoming missions is highly dependent on the availability of photometric redshifts. The purpose of this thesis is to provide innovative methods for photometric redshift estimation. Two models are proposed. The first is fully-automatized, based on the combination of a convolutional neural network with a mixture density network, to predict probabilistic multimodal redshifts directly from images. The second model is features-based, performing a massive combination of photometric parameters to apply a forward selection in a huge feature space. The proposed models perform very efficiently compared to some of the most common models used in the literature. An important part of the work is dedicated to the correct estimation of the errors and prediction quality. The proposed models are very general and can be applied to different topics in astronomy and beyond.