Show simple item record  

dc.contributor.authorRau, Michael M.en_NZ
dc.contributor.authorSeitz, Stellaen_NZ
dc.contributor.authorFrank, Eibeen_NZ
dc.contributor.authorBrimioulee, Fabriceen_NZ
dc.contributor.authorFriedrich, Oliveren_NZ
dc.contributor.authorGruen, Danielen_NZ
dc.contributor.authorHoyle, Benen_NZ
dc.date.accessioned2015-08-06T01:50:38Z
dc.date.available2015en_NZ
dc.date.available2015-08-06T01:50:38Z
dc.date.issued2015en_NZ
dc.identifier.citationRau, M. M., Seitz, S., Frank, E., Brimioulee, F., Friedrich, O., Gruen, D., & Hoyle, B. (2015). Accurate photometric redshift probability density estimation - method comparison and application. Monthly Notices of the Royal Astronomical Society, 1–17.en
dc.identifier.issn1365-2966en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/9528
dc.description.abstractWe introduce an ordinal classification algorithm for photometric redshift estimation, which significantly improves the reconstruction of photometric redshift probability density functions (PDFs) for individual galaxies and galaxy samples. As a use case we apply our method to CFHTLS galaxies. The ordinal classification algorithm treats distinct redshift bins as ordered values, which improves the quality of photometric redshift PDFs, compared with non-ordinal classification architectures. We also propose a new single value point estimate of the galaxy redshift, that can be used to estimate the full redshift PDF of a galaxy sample. This method is competitive in terms of accuracy with contemporary algorithms, which stack the full redshift PDFs of all galaxies in the sample, but requires orders of magnitudes less storage space. The methods described in this paper greatly improve the log-likelihood of individual object redshift PDFs, when compared with a popular Neural Network code (ANNz). In our use case, this improvement reaches 50% for high redshift objects (z ≥ 0.75). We show that using these more accurate photometric redshift PDFs will lead to a reduction in the systematic biases by up to a factor of four, when compared with less accurate PDFs obtained from commonly used methods. The cosmological analyses we examine and find improvement upon are the following: gravitational lensing cluster mass estimates, modelling of angular correlation functions, and modelling of cosmic shear correlation functions.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherOxford University Press (OUP): Policy P - Oxford Open Option Aen_NZ
dc.relation.urihttp://arxiv.org/abs/1503.08215en_NZ
dc.rightsThis article has been accepted for publication in Monthly Notices of the Royal Astronomical Society ©: 2015 The Authors. Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved.
dc.subjectgalaxies
dc.subjectdistances and redshifts
dc.subjectcatalogues
dc.subjectsurveys
dc.titleAccurate photometric redshift probability density estimation - method comparison and applicationen_NZ
dc.typeJournal Article
dc.identifier.doi10.1093/mnras/stv1567
dc.relation.isPartOfMonthly Notices of the Royal Astronomical Societyen_NZ
pubs.begin-page1
pubs.elements-id128828
pubs.end-page17
pubs.issue4en_NZ
pubs.publisher-urlhttp://arxiv.org/abs/1503.08215en_NZ
pubs.volume452en_NZ


Files in this item

This item appears in the following Collection(s)

Show simple item record