Disentangling modified gravity and galaxy bias with field-level inference
We present a field-level inference framework for testing gravity with the large-scale structure that exploits the full information content 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 likelihood analysis directly on the three-dimensional galaxy number counts field, jointly constraining MG and bias parameters using both amplitudes and phases. As an illustrative application, we analyse mock data in the context of the $f(R)$ theory of gravity and a non-linear galaxy bias model. Non-linear structure formation is modelled using the COmoving Lagrangian Acceleration (COLA) method 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 fixed and known initial phases, including non-Gaussian and phase information yields tighter constraints on both $f_{R0}$ and the primary bias parameter, $\beta$, relative to the 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 models 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 with next-generation surveys.