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)
Explore related subjects
Keep this discovery
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
Cite the original work for its findings. Save a collection to share your selection of sources.