Exploring the signature of assembly bias and modified gravity using small-scale clusterings of galaxies

TL;DR

This study demonstrates that a simple halo velocity bias model applied to galaxy clustering data can effectively identify signatures of assembly bias and modified gravity, providing a promising tool for cosmological investigations.

Contribution

The paper introduces and validates a halo velocity bias emulator that detects assembly bias effects and modified gravity signatures in galaxy clustering data.

Findings

Accurate recovery of cosmological parameters when considering assembly bias.

Ignoring assembly bias biases parameter constraints significantly.

Effective identification of modified gravity models through velocity bias deviations.

Abstract

We apply a halo velocity bias model, $\gamma_{f}$, within the Aemulus simulation suite for General Relativity (GR) to investigate its efficacy in identifying the signature of assembly bias and Modified Gravity (MG). In the investigation of assembly bias, utilizing galaxy clustering data ranging from scales of $0.1 - 60 \text{Mpc}\,h^{-1}$, we discover that our emulator model accurately recreates the cosmological parameters, $\Omega_m$ and $\sigma_8$, along with the velocity bias $\gamma_{f}$, staying well within the 1-$\sigma$ error margins, provided that assembly bias is considered. Ignoring assembly bias can considerably alter our model constraints on parameters $\Omega_m$ and $\sigma_8$ if the test sample includes assembly bias. Using our emulator for MG simulations, which encompasses two Dvali-Gabadadze-Porrati models (DGP; N1, N5) and two $f(R)$ models (F4, F6), we can effectively identify a robust signature of modified gravity, for models such as DGP-N1 and $f(R)$-F4, as indicated by a noticeable deviation of $\gamma_{f}$ from unity. Using the velocity dispersion of dark matter halos to effectively represent the underlying strength of the velocity field of these MG simulations, we find that the simple $\gamma_{f}$ model can recover the truth with minimal bias. These evaluations indicate that our simple halo-velocity bias model is capable of detecting significant MG characteristics, although additional methodologies should be pursued to improve model constraints.