title: Probabilistic photometric redshift estimation in massive digital sky surveys via machine learning
creator: D'Isanto, Antonio
subject: ddc-004
subject: 004 Data processing Computer science
subject: ddc-520
subject: 520 Astronomy and allied sciences
subject: ddc-530
subject: 530 Physics
description: 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.
date: 2019
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/26000/1/thesis_disanto.pdf
identifier: DOI:10.11588/heidok.00026000
identifier: urn:nbn:de:bsz:16-heidok-260000
identifier:   D'Isanto, Antonio  (2019) Probabilistic photometric redshift estimation in massive digital sky surveys via machine learning.  [Dissertation]     
relation: https://archiv.ub.uni-heidelberg.de/volltextserver/26000/
rights: info:eu-repo/semantics/openAccess
rights: http://archiv.ub.uni-heidelberg.de/volltextserver/help/license_urhg.html
language: eng