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• W2959038863 abstract "ABSTRACT The interpretation of redshift surveys requires modelling the relationship between large-scale fluctuations in the observed number density of tracers, δh, and the underlying matter density, δ. Bias models often express δh as a truncated series of integro-differential operators acting on δ, each weighted by a bias parameter. Due to the presence of ‘composite operators’ (obtained by multiplying fields evaluated at the same spatial location), the linear bias parameter measured from clustering statistics does not coincide with that appearing in the bias expansion. This issue can be cured by re-writing the expansion in terms of ‘renormalized’ operators. After providing a pedagogical and comprehensive review of bias renormalization in perturbation theory, we generalize the concept to non-perturbative dynamics and successfully apply it to dark-matter haloes extracted from a large suite of N-body simulations. When comparing numerical and perturbative results, we highlight the effect of the window function employed to smooth the random fields. We then measure the bias parameters as a function of halo mass by fitting a non-perturbative bias model (both before and after applying renormalization) to the cross spectrum $P_{delta _mathrm{h}delta }(k)$. Finally, we employ Bayesian model selection to determine the optimal operator set to describe $P_{delta _mathrm{h}delta }(k)$ for $klt 0.2, h$ Mpc−1 at redshift z = 0. We find that it includes δ, ∇2δ, δ2 and the square of the traceless tidal tensor, s2. Considering higher order terms (in δ) leads to overfitting as they cannot be precisely constrained by our data. We also notice that next-to-leading-order perturbative solutions are inaccurate for k ≳ 0.1 h Mpc−1." @default.
• W2959038863 created "2019-07-23" @default.
• W2959038863 creator A5061637450 @default.
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• W2959038863 date "2019-12-11" @default.
• W2959038863 modified "2023-10-15" @default.
• W2959038863 title "Renormalization of linear halo bias in N-body simulations" @default.
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• W2959038863 doi "https://doi.org/10.1093/mnras/stz3469" @default.
• W2959038863 hasPublicationYear "2019" @default.
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