What question did this study set out to answer?

To explore a non-parametric approach to measure galaxy bias directly from simulations, revealing its Gaussian nature.

February 5, 20260 citations

Gaussian Lagrangian galaxy bias

JSJens StückerUniversity of Vienna MPMarcos Pellejero-IbáñezUK Astronomy Technology Centre RVRodrigo VoivodicUniversidade de São Paulo

Key Points

To explore a non-parametric approach to measure galaxy bias directly from simulations, revealing its Gaussian nature.
Conducted simulation experiments to measure the Lagrangian bias function non-parametrically.
Introduced a Gaussian bias model with key properties for predicting galaxy formation probabilities.
Compared the new model's performance with traditional parametric bias models.
The Gaussian bias function shows close adherence to a Gaussian shape for various halo mass selections.
The new model predicts strictly positive probabilities for galaxy formation, unlike traditional methods.
Demonstrates better performance in describing the galaxy environment distribution compared to conventional expansion methods.

Abstract

Understanding galaxy bias – that is, the statistical relation between matter and galaxies – is of key importance for extracting cosmological information from galaxy surveys. While the ‘bias function’, f – the probability of forming galaxies in a region with a given density field – is usually approximated through a parametric expansion, we show here that it can also be measured directly from simulations in a non-parametric way. Our measurements show that the Lagrangian bias function is very close to a Gaussian for halo selections of any mass. Therefore, we introduce a new Gaussian bias model that has several intriguing properties: (1) it predicts only strictly positive probabilities, f > 0 (unlike expansion models), (2) it has a simple analytic re-normalised form, and (3) it behaves gracefully in many scenarios in which the classical expansion converges poorly. We show that the Gaussian bias model generally describes the galaxy environment distribution, p(δ|g), the scale-dependent bias function, f, and the re-normalised bias function, F, of haloes and galaxies as well as a second-order expansion with the same number of parameters, or significantly better than it. We suggest that a Gaussian bias approach may enhance the range of validity of bias schemes in which the canonical expansion converges poorly, and further that it may make new applications possible, since it guarantees the positivity of predicted galaxy densities.

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