Journal article
Optimising marked power spectra for cosmology
- Abstract:
- Marked power spectra provide a computationally efficient way to extract non-Gaussian information from the matter density field using the usual analysis tools developed for the power spectrum without the need for explicit calculation of higher-order correlators. In this work, we explore the optimal form of the mark function used for re-weighting the density field, to maximally constrain cosmology. We show that adding to the mark function or multiplying it by a constant leads to no additional information gain, which significantly reduces our search space for optimal marks. We quantify the information gain of this optimal function and compare it against mark functions previously proposed in the literature. We find that we can gain around ∼2 times smaller errors in 𝜎8 and ∼4 times smaller errors in Ω𝑚 compared to using the traditional power spectrum alone, an improvement of ∼60% compared to other proposed marks when applied to the same data set.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 2.0MB, Terms of use)
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- Publisher copy:
- 10.1093/mnras/stae2492
Authors
+ Royal Society
More from this funder
- Funder identifier:
- https://ror.org/03wnrjx87
- Grant:
- RGS\R1\221167
+ Science and Technology Facilities Council
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- Funder identifier:
- https://ror.org/057g20z61
- Grant:
- ST/W000903/1
- Publisher:
- Oxford University Press
- Journal:
- Monthly Notices of the Royal Astronomical Society More from this journal
- Volume:
- 535
- Issue:
- 4
- Pages:
- 3129–3140
- Publication date:
- 2024-11-06
- Acceptance date:
- 2024-11-01
- DOI:
- EISSN:
-
1365-2966
- ISSN:
-
0035-8711
- Language:
-
English
- Keywords:
- Pubs id:
-
2054950
- Local pid:
-
pubs:2054950
- Deposit date:
-
2024-11-06
Terms of use
- Copyright holder:
- Cowell et al
- Copyright date:
- 2024
- Rights statement:
- © 2024 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
- Licence:
- CC Attribution (CC BY)
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