Journal Article

Path integral marginalization for cosmology: scale-dependent galaxy bias and intrinsic alignments

T. D. Kitching and A. N. Taylor

in Monthly Notices of the Royal Astronomical Society

Published on behalf of The Royal Astronomical Society

Volume 410, issue 3, pages 1677-1686
Published in print January 2011 | ISSN: 0035-8711
Published online January 2011 | e-ISSN: 1365-2966 | DOI:
Path integral marginalization for cosmology: scale-dependent galaxy bias and intrinsic alignments

More Like This

Show all results sharing this subject:

  • Astronomy and Astrophysics


Show Summary Details


We present a path integral likelihood formalism that extends parametrized likelihood analyses to include continuous functions. The method finds the maximum-likelihood point in function-space, and marginalizes over all possible functions, under the assumption of a Gaussian-distributed function-space. We apply our method to the problem of removing unknown systematic functions in two topical problems for dark energy research: scale-dependent galaxy bias in redshift surveys and galaxy intrinsic alignments in cosmic shear surveys. We find that scale-dependent galaxy bias will degrade information on cosmological parameters unless the fractional variance in the bias function is known to 10 per cent. Measuring and removing intrinsic alignments from cosmic shear surveys with a flat prior can reduce the dark energy figure of merit by 20 per cent, however provided that the scale and redshift dependence is known to better than 10 per cent with a Gaussian prior, the dark energy figure of merit can be enhanced by a factor of 2 with no extra assumptions.

Keywords: methods: analytical; methods: data analysis; methods: statistical; cosmology: theory; large-scale structure of Universe

Journal Article.  6396 words.  Illustrated.

Subjects: Astronomy and Astrophysics

Full text: subscription required

How to subscribe Recommend to my Librarian

Users without a subscription are not able to see the full content. Please, subscribe or login to access all content.