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HOD-informed prior for EFT-based full-shape analyses of LSS
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HOD-informed prior for EFT-based full-shape analyses of LSS
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To improve the performance of full-shape analyses of large-scale structure, we consider using a halo occupation distribution (HOD)-informed prior for the effective field theory (EFT) nuisance parameters. We generate 320 000 mock galaxy catalogs using 10 000 sets of HOD parameters across 32 simulation boxes with different cosmologies. We measure and fit the redshift-space power spectra using a fast emulator of the EFT model, and the resulting best-fit EFT parameter distributions are used to create the prior. This prior effectively constrains the EFT nuisance parameter space, limiting it to the space of HOD-mocks that can be well fit by a EFT model. We have tested the stability of the prior under different configurations, including the effect of varying the HOD sample distribution and the inclusion of the hexadecapole moment. We find that our HOD-informed prior and the cosmological parameter constraints derived using it are robust. While cosmological fits using the standard EFT prior suffer from prior effects, sometimes failing to recover the true cosmology within Bayesian credible intervals, the HOD-informed prior mitigates these issues and significantly improves cosmological parameter recovery for $\Lambda$CDM and beyond. This work lays the foundation for better full-shape large-scale structure analyses in current and upcoming galaxy surveys, making it a valuable tool for addressing key questions in cosmology.
Forward citations
Cited by 8 Pith papers
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A hybrid Jeffreys+baseline prior removes multi-σ prior-volume projection in DESI DR1 full-shape fits of H0, w0, and wa, yielding late-time expansion constraints consistent with HOD-informed Bayesian and frequentist analyses.
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Simulation-based priors applied to DESI DR1 full-shape data sharpen cosmological constraints (σ8 error halved) and yield Mν<0.090 eV in w0waCDM, but the results depend on HOD modeling assumptions.
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DESI 2024 V: Full-Shape Galaxy Clustering from Galaxies and Quasars
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