Neural network-based model of galaxy power spectrum: fast full-shape galaxy power spectrum analysis Article Swipe
S. Trusov
,
Pauline Zarrouk
,
Shaun Cole
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1093/mnras/staf285
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1093/mnras/staf285
We present a neural network-based emulator for the galaxy redshift-space power spectrum that enables several orders of magnitude acceleration in the galaxy clustering parameter inference, while preserving 3$\sigma$ accuracy better than 0.5 per cent up to $k_{\mathrm{max}}$ = 0.25 $\, h\text{Mpc}^{-1}$ within Lambda-cold dark matter ($\Lambda$CDM) and around 0.5 per cent $w_0$–$w_a$CDM. Our surrogate model only emulates the galaxy bias-invariant terms of one-loop perturbation theory predictions, these terms are then combined analytically with galaxy bias terms, counter-terms, and stochastic terms in order to obtain the non-linear redshift-space galaxy power spectrum. This allows us to avoid any galaxy bias prescription in the training of the emulator, which makes it more flexible. Moreover, we include the redshift $z \in [0,1.4]$ in the training which further avoids the need for re-training the emulator. We showcase the performance of the emulator in recovering the cosmological parameters of $\Lambda$CDM by analysing the suite of 25 AbacusSummit simulations that mimic the Dark Energy Spectroscopic Instrument luminous red galaxies at $z=0.5$ and 0.8, together as the emission line galaxies at $z=0.8$. We obtain similar performance in all cases, demonstrating the reliability of the emulator for any galaxy sample at any redshift in $0 \lt z \lt 1.4$. We will make our emulator public at github repository.
Related Topics
Concepts
Physics
Galaxy
Astrophysics
Spectral density
Interacting galaxy
Power (physics)
Type-cD galaxy
Disc galaxy
Galaxy merger
Astronomy
Galaxy formation and evolution
Quantum mechanics
Statistics
Mathematics
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- Language
- en
- Landing Page
- https://doi.org/10.1093/mnras/staf285
- OA Status
- gold
- Cited By
- 4
- References
- 40
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4407710480Canonical identifier for this work in OpenAlex
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https://doi.org/10.1093/mnras/staf285Digital Object Identifier
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Neural network-based model of galaxy power spectrum: fast full-shape galaxy power spectrum analysisWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-02-17Full publication date if available
- Authors
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S. Trusov, Pauline Zarrouk, Shaun ColeList of authors in order
- Landing page
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https://doi.org/10.1093/mnras/staf285Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://doi.org/10.1093/mnras/staf285Direct OA link when available
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Physics, Galaxy, Astrophysics, Spectral density, Interacting galaxy, Power (physics), Type-cD galaxy, Disc galaxy, Galaxy merger, Astronomy, Galaxy formation and evolution, Quantum mechanics, Statistics, MathematicsTop concepts (fields/topics) attached by OpenAlex
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4Total citation count in OpenAlex
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2025: 4Per-year citation counts (last 5 years)
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