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arXiv · 2608.26262

Assembly bias from nuisance to probe II: Salvaging linear clustering from DESI and SDSS data

Abstract

Galactic conformity links the properties of neighbouring galaxies and is usually interpreted as a signature of assembly-biased galaxy occupation. We study projected and redshift-space compensated conformity statistics in colour-selected DESI BGS and SDSS MGS samples. These combine correlations of related galaxy populations, suppressing shared nonlinear clustering contributions while retaining a differential large-scale response. We test whether this exposes the linear matter correlation-function shape where ordinary colour-selected clustering has a scale-dependent nonlinear response. We split galaxies into red and blue subsamples and measure ordinary correlations, conformity, and compensated combinations in projection and redshift-space monopoles, using central primaries by default. We fit linear matter templates, diagnose the response with effective kernels, and compare with MTNG, FLAMINGO, and MDPL2--SAG. Ordinary high-colour clustering broadly follows the linear templates but develops a scale-dependent small-scale response. In DESI, $\Delta f_0(s)$, projected $\Delta f(r_p)$, and $C_w(r_p)$ track the linear matter shape substantially further into the nonlinear regime, consistent with suppression of nonlinear clustering modes while leaving a linear-matter-like mode visible. The effect is stronger for central-primary and dense samples. Projected effective kernels are enhanced at low line-of-sight separations, helping the residual resemble $\xi_{\rm mm}^{\rm lin}(r_p)$ rather than $w_{\rm mm}^{\rm lin}(r_p)$. Simulations reproduce the qualitative behaviour with model-dependent amplitudes and residual scale dependence. These results suggest that assembly-sensitive population differences can filter nonlinear clustering modes, although surviving nonlinear contamination must be calibrated before precision cosmological use. (abridged)

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Nelson Padilla, Dante Paz, Iván Lacerna. 2026-08-26. Assembly bias from nuisance to probe II: Salvaging linear clustering from DESI and SDSS data. https://arxiv.org/abs/2608.26262

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