Toward Accurate Modeling of Galaxy Clustering on Small Scales: Constraining the Galaxy-Halo Connection with Optimal Statistics
arXiv:2110.03701 · doi:10.3847/1538-4357/ac3a7c
Abstract
Applying halo models to analyze the small-scale clustering of galaxies is a proven method for characterizing the connection between galaxies and their host halos. Such works are often plagued by systematic errors or are limited to clustering statistics which can be predicted analytically. In this work, we employ a numerical mock-based modeling procedure to examine the clustering of SDSS DR7 galaxies. We apply a standard halo occupation distribution (HOD) model to dark-matter-only simulations with a LCDM cosmology. To constrain the theoretical models, we utilize a combination of galaxy number density and selected scales of the projected correlation function, redshift-space correlation function, group multiplicity function, average group velocity dispersion, mark correlation function, and counts-in-cells statistics. We design an algorithm to choose an optimal combination of measurements that yields tight and accurate constraints on our model parameters. Compared to previous work using fewer clustering statistics, we find significant improvement in the constraints on all parameters of our halo model for two different luminosity-threshold galaxy samples. Most interestingly, we obtain unprecedented high-precision constraints on the scatter in the relationship between galaxy luminosity and halo mass. However, our best-fit model results in significant tension (>4 sigma) for both samples, indicating the need to add second-order features to the standard HOD model. To guarantee the robustness of these results, we perform an extensive analysis of the systematic and statistical errors in our modeling procedure, including a first-of-its-kind study of the sensitivity of our constraints to changes in the halo mass function due to baryonic physics.
36 pages, 17 figures, submitted to ApJ
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Cited by in corpus (6)
- The Aemulus Project V: Cosmological constraint from small-scale clustering of BOSS galaxies
- Evidence of Galaxy Assembly Bias in SDSS DR7 Galaxy Samples from Count Statistics
- The Aemulus Project VI: Emulation of beyond-standard galaxy clustering statistics to improve cosmological constraints
- Toward Accurate Modeling of Galaxy Clustering on Small Scales: Halo Model Extensions and Lingering Tension
- Informed total-error-minimizing priors: Interpretable cosmological parameter constraints despite complex nuisance effects
- Constraining Power of Wavelet vs. Power Spectrum Statistics for CMB Lensing and Weak Lensing with Learned Binning