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Bayesian photometric redshifts with empirical training sets

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dc.contributor.authorWolf, Christian
dc.date.accessioned2015-12-13T22:17:58Z
dc.date.issued2009
dc.date.updated2016-02-24T10:03:22Z
dc.description.abstractWe combine in a single framework the two complementary benefits of χ2 template fits and empirical training sets used e.g. in neural nets: χ2 is more reliable when its probability density functions (PDFs) are inspected for multiple peaks, while empirical
dc.identifier.issn0035-8711
dc.identifier.urihttp://hdl.handle.net/1885/71414
dc.publisherBlackwell Publishing Ltd
dc.sourceMonthly Notices of the Royal Astronomical Society
dc.subjectKeywords: Methods: statistical; Surveys; Techniques: photometric
dc.titleBayesian photometric redshifts with empirical training sets
dc.typeJournal article
local.bibliographicCitation.issue1
local.bibliographicCitation.lastpage533
local.bibliographicCitation.startpage520
local.contributor.affiliationWolf, Christian, College of Physical and Mathematical Sciences, ANU
local.contributor.authoruidWolf, Christian, u5281441
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.identifier.absfor020103 - Cosmology and Extragalactic Astronomy
local.identifier.ariespublicationU3488905xPUB2701
local.identifier.citationvolume397
local.identifier.doi10.1111/j.1365-2966.2009.14953.x
local.identifier.scopusID2-s2.0-67650410051
local.type.statusPublished Version

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