Orateur
Description
Precision constraints on dark energy from large-scale-structure surveys require large ensembles of cosmological simulations for covariance estimation, forward modelling, and simulation-based inference. Producing such ensembles at Euclid volumes remains computationally challenging with conventional $N$-body methods. We present a fast and scalable framework based on the spatially discretised COmoving Lagrangian Acceleration (sCOLA) method that reduces this cost by evolving only the spacetime region contributing to the observed lightcone.
sCOLA decomposes large simulation volumes into independent tiles. Only tiles intersecting the survey volume are evolved, and each tile is stopped once it exits the observer's past lightcone. The resulting algorithm is perfectly parallel and avoids unnecessary spatial and temporal evolution. We validate sCOLA lightcones against $\texttt{Gadget-4}$, finding percent-level agreement in the dark-matter power spectrum up to $k=0.5\,h\,\mathrm{Mpc}^{-1}$ and agreement at the $\sim10\%$ level in halo statistics. For our target lightcone configurations, this approach yields speed-ups of up to $\sim10^3\!-\!10^4$ relative to Tree-PM simulations.
Using this framework, we produced Silmarilli, a suite of $\sim100$ independent lightcone simulations covering a volume equivalent to the Galactic North Cap of the Euclid Wide Survey over $0.84<z<1.88$, with a particle mass resolution of $10^{10}\,M_\odot$, for a total cost of 6 million CPU hours.
Silmarilli provides survey-scale dark-matter and halo realizations for empirical covariance estimation, emulator training, and simulation-based inference, demonstrating how sCOLA can make large ensembles of Euclid-scale lightcones computationally tractable for precision cosmology.