Papers › Quijote PNG: The information content of the halo power spectrum and bispectrum

Quijote PNG: The information content of the halo power spectrum and bispectrum

30 Jun 2022arXiv:2206.15450links table onlyarchive 2025-07-28

William R Coulton, Francisco Villaescusa-Navarro, Drew Jamieson, Marco Baldi, Gabriel Jung, Dionysios Karagiannis, Michele Liguori, Licia Verde, Benjamin D. Wandelt

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We investigate how much can be learnt about four types of primordial non-Gaussianity (PNG) from small-scale measurements of the halo field. Using the QUIJOTE-PNG simulations, we quantify the information content accessible with measurements of the halo power spectrum monopole and quadrupole, the matter power spectrum, the halo-matter cross spectrum and the halo bispectrum monopole. This analysis is the first to include small, non-linear scales, up to kₘₐₓ=0.5 h/Mpc, and to explore whether these scales can break degeneracies with cosmological and nuisance parameters making use of thousands of N-body simulations. We perform all the halo measurements in redshift space with a single sample comprised of all halos with mass >3.2 ×10¹³ h⁻¹M_⊙. For local PNG, measurements of the scale dependent bias effect from the power spectrum using sample variance cancellation provide significantly tighter constraints than measurements of the halo bispectrum. In this case measurements of the small scales add minimal additional constraining power. In contrast, the information on equilateral and orthogonal PNG is primarily accessible through the bispectrum. For these shapes, small scale measurements increase the constraining power of the halo bispectrum by up to ×4, though the addition of scales beyond k≈0.3 h/Mpc improves constraints largely through reducing degeneracies between PNG and the other parameters. These degeneracies are even more powerfully mitigated through combining power spectrum and bispectrum measurements. However even with combined measurements and small scale information, equilateral non-Gaussianity remains highly degenerate with σ₈ and our bias model.

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