Beschreibung
We apply for the first time an alternative approach based on the Renormalization Group (RG) formalism, proposed in Rubira and Schmidt 2023, in which the bias parameters become explicitly dependent on the cut-off scale and are naturally suited for EFT-based field-level inference. Specifically, we use the Lagrangian perturbation theory forward-modeling code $\texttt{LEFTfield}$ to generate mock data at a fixed cut-off scale, and then estimate the running of the bias parameters at smaller cut-off scales using an EFT-based likelihood. In addition, we perform a conventional n-point correlation function analysis to measure the bias parameters in the large-scale limit for comparison with the RG predictions. This study serves as a proof of concept for the application of RG flow in galaxy bias, demonstrating the feasibility of measuring bias parameters with this novel approach.