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dc.contributor.authorObreschkow, D.
dc.contributor.authorMurray, Steven
dc.contributor.authorRobotham, A.
dc.contributor.authorWestmeier, T.
dc.date.accessioned2018-05-18T08:01:22Z
dc.date.available2018-05-18T08:01:22Z
dc.date.created2018-05-18T00:23:13Z
dc.date.issued2018
dc.identifier.citationObreschkow, D. and Murray, S. and Robotham, A. and Westmeier, T. 2018. Eddington's demon: Inferring galaxy mass functions and other distributions from uncertain data. Monthly Notices of the Royal Astronomical Society. 474 (4): pp. 5500-5522.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/68228
dc.identifier.doi10.1093/mnras/stx3155
dc.description.abstract

We present a general modified maximum likelihood (MML) method for inferring generative distribution functions from uncertain and biased data. The MML estimator is identical to, but easier and many orders of magnitude faster to compute than the solution of the exact Bayesian hierarchical modelling of all measurement errors. As a key application, this method can accurately recover the mass function (MF) of galaxies, while simultaneously dealing with observational uncertainties (Eddington bias), complex selection functions and unknown cosmic large-scale structure. The MML method is free of binning and natively accounts for small number statistics and non-detections. Its fast implementation in the R-package dftools is equally applicable to other objects, such as haloes, groups, and clusters, as well as observables other than mass. The formalism readily extends to multidimensional distribution functions, e.g. a Choloniewski function for the galaxy mass-angular momentum distribution, also handled by dftools. The code provides uncertainties and covariances for the fitted model parameters and approximate Bayesian evidences. We use numerous mock surveys to illustrate and test the MML method, as well as to emphasize the necessity of accounting for observational uncertainties in MFs of modern galaxy surveys.

dc.publisherOxford University Press
dc.titleEddington's demon: Inferring galaxy mass functions and other distributions from uncertain data
dc.typeJournal Article
dcterms.source.volume474
dcterms.source.number4
dcterms.source.startPage5500
dcterms.source.endPage5522
dcterms.source.issn0035-8711
dcterms.source.titleMonthly Notices of the Royal Astronomical Society
curtin.note

This article has been accepted for publication in Monthly Notices of the Royal Astronomical Society ©: 2017 The Author(s). Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved.

curtin.departmentCurtin Institute of Radio Astronomy (Physics)
curtin.accessStatusOpen access


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