Show simple item record

dc.contributor.authorMurray, Steven
dc.contributor.authorPower, C.
dc.contributor.authorRobotham, A.
dc.date.accessioned2017-08-24T02:23:16Z
dc.date.available2017-08-24T02:23:16Z
dc.date.created2017-08-23T07:21:42Z
dc.date.issued2015
dc.identifier.citationMurray, S. and Power, C. and Robotham, A. 2015. Modelling galaxy populations in the era of big data. Proceedings of the International Astronomical Union. 10: pp. 304-306.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/56295
dc.identifier.doi10.1017/S1743921314010710
dc.description.abstract

© International Astronomical Union 2015. The coming decade will witness a deluge of data from next generation galaxy surveys such as the Square Kilometre Array and Euclid. How can we optimally and robustly analyse these data to maximise scientific returns from these surveys? Here we discuss recent work in developing both the conceptual and software frameworks for carrying out such analyses and their application to the dark matter halo mass function. We summarise what we have learned about the HMF from the last 10 years of precision CMB data using the open-source HMFcalc framework, before discussing how this framework is being extended to the full Halo Model.

dc.titleModelling galaxy populations in the era of big data
dc.typeJournal Article
dcterms.source.volume10
dcterms.source.startPage304
dcterms.source.endPage306
dcterms.source.issn1743-9213
dcterms.source.titleProceedings of the International Astronomical Union
curtin.departmentCurtin Institute of Radio Astronomy (Physics)
curtin.accessStatusFulltext not available


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record