| Issue |
A&A
Volume 702, October 2025
|
|
|---|---|---|
| Article Number | A120 | |
| Number of page(s) | 12 | |
| Section | Cosmology (including clusters of galaxies) | |
| DOI | https://doi.org/10.1051/0004-6361/202554621 | |
| Published online | 14 October 2025 | |
Emulating the non-linear matter power spectrum in mixed axion dark matter models
Institute of Theoretical Astrophysics, University of Oslo, PO Box 1029 Blindern, 0315 Oslo, Norway
⋆ Corresponding author: dennisfr@uio.no
Received:
18
March
2025
Accepted:
12
August
2025
In order to constrain ultra light dark matter models with current and near future weak lensing surveys, we need the predictions for the non-linear dark matter power spectrum. This is commonly extracted from numerical simulations or from semi-analytical methods. For ultra light dark matter models, such numerical simulations are often very expensive due to the need of having a very high force resolution, often limiting the simulations to very small simulation boxes that do not contain very large scales. In this work, we take a different approach by relying on fast approximate N-body simulations. In these simulations, axion physics is only included in the initial conditions, allowing us to run a large number of simulations with varying axion and cosmological parameters. From our simulation suite, we used machine learning tools to create an emulator for the ratio of the dark matter power spectrum in mixed axion models – models where dark matter is a combination of CDM and axion – to that of ΛCDM. The resulting emulator only needs to be combined with existing emulators for ΛCDM to be able to be used in parameter constraints. We compared the emulator to semi-analytical methods, but a more thorough test to full simulations to verify the true accuracy of this approach is not possible at the present time and is left for future work.
Key words: dark matter / large-scale structure of Universe
© The Authors 2025
Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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