Probabilistic Lagrangian bias estimators and the cumulant bias expansion

Abstract

The spatial distribution of galaxies is a highly complex phenomenon currently impossible to predict deterministically. However, by using a statistical $\text{bias}$ relation, it becomes possible to robustly model the average abundance of galaxies as a function of the underlying matter density field. Understanding the properties and parametric description of the bias relation is key to extract cosmological information from future galaxy surveys. Here, we contribute to this topic primarily in two ways: (1) We develop a new set of probabilistic estimators for bias parameters using the moments of the Lagrangian galaxy environment distribution. These estimators include spatial corrections at different orders to measure bias parameters independently of the damping scale. We report robust measurements of a variety of bias parameters for haloes, including the tidal bias and its dependence with spin at a fixed mass. (2) We propose an alternative formulation of the bias expansion in terms of "cumulant bias parameters" that describe the response of the logarithmic galaxy density to large-scale perturbations. We find that cumulant biases of haloes are consistent with zero at orders $n>2$. This suggests that: (i) previously reported bias relations at order $n>2$ are an artefact of the entangled basis of the canonical bias expansion; (ii) the convergence of the bias expansion may be improved by phrasing it in terms of cumulants; (iii) the bias function is very well approximated by a Gaussian -- an avenue which we explore in a companion paper.

26 pages, 16 figures, submitted to A&A

References in corpus (36)

• The EAGLE project: Simulating the evolution and assembly of galaxies and their environments
• Introducing the Illustris Project: Simulating the coevolution of dark and visible matter in the Universe
• The Completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: Cosmological Implications from two Decades of Spectroscopic Surveys at the Apache Point observatory
• Simba: Cosmological Simulations with Black Hole Growth and Feedback
• The Cosmological Analysis of the SDSS/BOSS data from the Effective Field Theory of Large-Scale Structure
• Cosmological Parameters from the BOSS Galaxy Power Spectrum
• Assembly bias in the clustering of dark matter haloes
• Halo assembly bias and its effects on galaxy clustering
• The CAMELS project: Cosmology and Astrophysics with MachinE Learning Simulations
• The BACCO Simulation Project: Exploiting the full power of large-scale structure for cosmology
• Blinded challenge for precision cosmology with large-scale structure: results from effective field theory for the redshift-space galaxy power spectrum
• Large-scale dark matter simulations
• Efficient Cosmological Analysis of the SDSS/BOSS data from the Effective Field Theory of Large-Scale Structure
• Modeling scale-dependent bias on the baryonic acoustic scale with the statistics of peaks of Gaussian random fields
• Consistent Modeling of Velocity Statistics and Redshift-Space Distortions in One-Loop Perturbation Theory
• Separate Universe Simulations
• The Gaussian streaming model and Lagrangian effective field theory
• Non-local Lagrangian bias
• Building disc structure and galaxy properties through angular momentum: The DARK SAGE semi-analytic model
• The cosmology dependence of galaxy clustering and lensing from a hybrid $N$-body-perturbation theory model
• Simulations and symmetries
• On the impact of galaxy bias uncertainties on primordial non-Gaussianity constraints
• A flexible subhalo abundance matching model for galaxy clustering in redshift space
• Galaxy bias from forward models: linear and second-order bias of IllustrisTNG galaxies
• The BACCO simulation project: biased tracers in real space
• The Bacco Simulation Project: Bacco Hybrid Lagrangian Bias Expansion Model in Redshift Space
• Aemulus $\nu$: Precise Predictions for Matter and Biased Tracer Power Spectra in the Presence of Neutrinos
• Priors on Lagrangian bias parameters from galaxy formation modelling
• Measuring the Tidal Response of Structure Formation: Anisotropic Separate Universe Simulations using TreePM
• The galaxy formation origin of the lensing is low problem
• Cosmological simulation in tides: power spectra and halo shape responses, and shape assembly bias
• The physical origins of low-mass spin bias
• Anisotropic separate universe simulations
• Modelling galaxy clustering in redshift space with a Lagrangian bias formalism and $N$-body simulations
• SHAMe-SF: Predicting the clustering of star-forming galaxies with an enhanced abundance matching model
• Gaussian Lagrangian Galaxy Bias

Cited by in corpus (1)

• Probabilistic Estimators of Lagrangian Shape Biases: Universal Relations and Physical Insights