Euclid is expected to establish new state-of-the-art constraints on extensions beyond the standard cosmological model by measuring the positions and shapes of billions of galaxies. Specifically, its goal is to shed light on the nature of dark matter and dark energy. Achieving this requires developing and validating advanced statistical tools and theoretical prediction software capable of testing extensions of the model. In this work, we describe how the Euclid likelihood pipeline, Cosmology Likelihood for Observables in Euclid (ļoe), has been extended to accommodate alternative cosmological models and to refine the theoretical modelling of Euclid primary probes. In particular, we describe the modifications made to ļoe to incorporate the magnification bias term into the spectroscopic two-point correlation function of galaxy clustering. Additionally, we explain the adaptations made to ļoe's implementation of Euclid primary photometric probes to account for massive neutrinos and modified gravity extensions. Finally, we present the validation of these ļoe modifications through dedicated forecasts on synthetic Euclid-like data by sampling the full posterior distribution and comparing with the results drawn from the literature. In conclusion, we have identified several functionalities with regard to beyond- modelling that could be further improved within ļoe. We also outline potential research directions to enhance the pipeline efficiency and flexibility through novel inference and machine learning techniques.
Goh et al. (Fri,) studied this question.