Dynamic zoom simulations of structure formation beyond standard cosmology

A thorough interpretation of the current and upcoming generation of cosmological observations requires unprecedented large-scale, high-resolution simulations that span multiple cosmological models and parameters. These simulations are extremely computationally demanding, and their realization poses a crucial technical challenge. We present beyond-ΛCDM implementations of the dynamic zoom simulations (DZSs) method, a performance-enhancing technique tailored for large-scale simulations that produce light-cone-like outputs. This approach dynamically decreases the resolution of a simulation in those regions that are not in causal connection with the observer, which substantially saves computational resources without directly affecting the physical properties within the light cone. We implemented the DZS algorithm in two state-of-the-art codes that support nonstandard cosmologies, namely modified f(R) gravity in Arepo and dark sector interactions in Gadget4. We then analyzed the result accuracy and performance gains across resolution, simulation volume, and cosmological model by comparing simulations performed with and without the DZS algorithm. We show that our DZSs reproduce the light cone halo mass function, sky-projected massmaps, and matter and weak lensing convergence power spectra with an accuracy of ≃ 0.1% or higher in most cases. In terms of performance, DZSs in our tests can save up to ∼ 50% runtime compared to the non-DZS counterparts. Moreover, scaling our results to larger simulated volumes suggests that performance gains could improve by an additional ∼ 20% at the resolution levels of current state-of-the-art cosmological simulations. The validation of the DZS algorithm in beyond-ΛCDM models demonstrates that this technique can enable cost-effective, large-scale (≳ 1 cGpc$/h$) simulations with state-of-the-art resolution, and provide the computational framework needed to constrain and help the interpretation of forthcoming observational data.