Unbiased estimates of the shapes of haloes using the positions of satellite galaxies

TL;DR

This paper introduces a method to correct for sampling noise bias in measuring dark matter halo shapes using satellite galaxy positions, enabling more accurate shape estimates with fewer satellites and lower-mass halos.

Contribution

The authors develop a bias correction technique for halo shape estimates derived from satellite galaxies, validated against simulations, improving measurement accuracy with limited satellite data.

Findings

Median projected axis ratio biased low by 0.31 with 5 satellites

Bias correction reduces residual bias to ~0.1

Method enables shape measurement at lower masses

Abstract

The shapes of dark matter haloes are sensitive to both cosmology and baryon physics, but are difficult to measure observationally. A promising way to constrain them is to use the positions of satellite galaxies as tracers of the underlying dark matter, but there are typically too few galaxies per halo for reliable shape estimates, resulting in biased shapes. We present a method to model sampling noise to correct for the shape bias. We compare our predicted median shape bias with that obtained from the FLAMINGO suite of simulations and find reasonable agreement. We check that our results are robust to resolution effects and baryonic feedback. We also explore the validity of our bias correction at various redshifts and we discuss how our method might be applied to observations in the future. We show that median projected halo axis ratios are on average biased low by 0.31 when they are traced by only 5 satellites. Using the satellite galaxies, the projected host halo axis ratio can be corrected with a residual bias of ~ 0.1, by accounting for sampling bias. Hence, about two-thirds of the projected axis ratio bias can be explained by sampling noise. This enables the statistical measurement of halo shapes at lower masses than previously possible. Our method will also allow improved estimates of halo shapes in cosmological simulations using fewer particles than currently required.