Modelling galaxy populations in the era of big data
dc.contributor.author | Murray, Steven | |
dc.contributor.author | Power, C. | |
dc.contributor.author | Robotham, A. | |
dc.date.accessioned | 2017-08-24T02:23:16Z | |
dc.date.available | 2017-08-24T02:23:16Z | |
dc.date.created | 2017-08-23T07:21:42Z | |
dc.date.issued | 2015 | |
dc.identifier.citation | Murray, 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.uri | http://hdl.handle.net/20.500.11937/56295 | |
dc.identifier.doi | 10.1017/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.title | Modelling galaxy populations in the era of big data | |
dc.type | Journal Article | |
dcterms.source.volume | 10 | |
dcterms.source.startPage | 304 | |
dcterms.source.endPage | 306 | |
dcterms.source.issn | 1743-9213 | |
dcterms.source.title | Proceedings of the International Astronomical Union | |
curtin.department | Curtin Institute of Radio Astronomy (Physics) | |
curtin.accessStatus | Fulltext not available |
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