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Modelling Galaxy Populations in the Era of Big Data

Published online by Cambridge University Press:  01 July 2015

S. G. Murray ,
C. Power  and
A. S. G. Robotham
S. G. Murray*
Affiliation:
ICRAR, University of Western Australia, 35 Stirling Highway, Crawley, Western Australia 6009, Australia ARC Centre of Excellence for All-Sky Astrophysics (CAASTRO)
C. Power
Affiliation:
ICRAR, University of Western Australia, 35 Stirling Highway, Crawley, Western Australia 6009, Australia ARC Centre of Excellence for All-Sky Astrophysics (CAASTRO)
A. S. G. Robotham
Affiliation:
ICRAR, University of Western Australia, 35 Stirling Highway, Crawley, Western Australia 6009, Australia
*
†[email protected]
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Abstract

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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.

Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 10 , Symposium S306: Statistical Challenges in 21st Century Cosmology , May 2014 , pp. 304 - 306
Copyright
Copyright © International Astronomical Union 2015 

References

Bhattacharya, S., Heitmann, K., White, M., et al. 2011, ApJ, 732, 122Google Scholar
Zehavi, I., Zheng, Z., Weinberg, D. H., et al. 2011, ApJ, 736, 59Google Scholar
Zheng, Z. 2004, ApJ, 610, 61CrossRefGoogle Scholar
Beutler, F., Blake, C., Colless, M., et al. 2013, MNRAS, 429, 3604Google Scholar
Murray, S. G., Power, C., & Robotham, A. S. G. 2013, MNRAS, 434, L61Google Scholar
Murray, S. G., Power, C., & Robotham, A. S. G. 2013, Astronomy and Computing, 3, 23Google Scholar
Vikhlinin, A., et al. 2009, ApJ, 692, 1060Google Scholar