Disentangling modified gravity and galaxy bias with field-level inference
Abstract
We present a field-level inference framework for testing gravity with large-scale structure, exploiting the full information of the galaxy distribution. Traditional analyses based on the power spectrum discard non-Gaussian and Fourier phase information, resulting in strong degeneracies between modified gravity (MG) and galaxy bias. Our approach overcomes this limitation by performing a Bayesian analysis directly on the three-dimensional galaxy number counts, jointly constraining MG and bias parameters using both amplitudes and phases. As an illustrative application, we analyse mock data in real space in the context of f(R) gravity and a non-linear galaxy bias model designed to mimic the real-data 2M++ BORG analysis of Jasche&Lavaux (2019). Non-linear structure formation is modelled using COmoving Lagrangian Acceleration (COLA) under different gravity strengths, parameterised by fR0. The resulting dark-matter fields are then mapped to mock galaxy catalogues via a non-linear bias prescription. We demonstrate that, with the initial phases assumed known, including non-Gaussian and phase information yields tighter constraints on both fR0 and the primary bias parameter β (corresponding to the linear galaxy bias on large scales), relative to power-spectrum-only analyses. Notably, the field-level approach breaks the degeneracies between MG and galaxy bias inherent to two-point statistics. Through a cosmic-web classification into voids, walls, filaments and clusters, we find that under-dense regions are the primary drivers in distinguishing gravity at the field level. Finally, we establish the robustness of our pipeline against variations in initial conditions, Poisson noise, and galaxy-field thresholding, providing a powerful path forward for field-level tests of gravity.
Related
- The automated calibration of simulated galaxy catalogues and the generation of realistic galaxy mocks for modified gravity scenarios
- Probing modified gravity with galaxy-cluster-lensed gravitational-wave time delays: impact of systematic effects
- Impact of galaxy intrinsic alignments on non-Gaussian weak lensing statistics for modified gravity
- Inductive Biases in Field-Level Cosmological Inference from Galaxy Catalogs
- Inductive Biases in Field-level Cosmological Inference from Galaxy Catalogs
BibTeX
@misc{hoyland2026disentangling,
title = {{Disentangling modified gravity and galaxy bias with field-level inference}},
author = {Sophie Hoyland and Daniela Saadeh and Kazuya Koyama and Harry Desmond},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2607.03514},
eprint = {2607.03514},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2607.03514},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Disentangling modified gravity and galaxy bias with field-level inference
AU - Sophie Hoyland
AU - Daniela Saadeh
AU - Kazuya Koyama
AU - Harry Desmond
PY - 2026
DA - 2026/10/01
PB - arXiv
DO - 10.48550/arxiv.2607.03514
UR - https://arxiv.org/abs/2607.03514
AB - We present a field-level inference framework for testing gravity with large-scale structure, exploiting the full information of the galaxy distribution. Traditional analyses based on the power spectrum discard non-Gaussian and Fourier phase information, resulting in strong degeneracies between modified gravity (MG) and galaxy bias. Our approach overcomes this limitation by performing a Bayesian analysis directly on the three-dimensional galaxy number counts, jointly constraining MG and bias parameters using both amplitudes and phases. As an illustrative application, we analyse mock data in real space in the context of f(R) gravity and a non-linear galaxy bias model designed to mimic the real-data 2M++ BORG analysis of Jasche&Lavaux (2019). Non-linear structure formation is modelled using COmoving Lagrangian Acceleration (COLA) under different gravity strengths, parameterised by f_R0. The resulting dark-matter fields are then mapped to mock galaxy catalogues via a non-linear bias prescription. We demonstrate that, with the initial phases assumed known, including non-Gaussian and phase information yields tighter constraints on both f_R0 and the primary bias parameter β (corresponding to the linear galaxy bias on large scales), relative to power-spectrum-only analyses. Notably, the field-level approach breaks the degeneracies between MG and galaxy bias inherent to two-point statistics. Through a cosmic-web classification into voids, walls, filaments and clusters, we find that under-dense regions are the primary drivers in distinguishing gravity at the field level. Finally, we establish the robustness of our pipeline against variations in initial conditions, Poisson noise, and galaxy-field thresholding, providing a powerful path forward for field-level tests of gravity.
ER -