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The Rosetta Stone Project. I. A suite of radiative magnetohydrodynamics simulations of high-mass star-forming clumps (2507.08436v1)

Published 11 Jul 2025 in astro-ph.SR, astro-ph.GA, and astro-ph.IM

Abstract: Context. Star formation and, in particular, high-mass star formation are key astrophysical processes that are far from being fully understood. Unfortunately, progress in these fields is slow because observations are hard to interpret as they cannot be directly compared to numerical simulations. Synthetic observations are therefore necessary to better constrain the models. Aims. With the Rosetta Stone project, we aim to develop an end-to-end pipeline to compare star formation simulations with observations as accurately as possible in order to study the evolution from clumps scales to stars. Methods. Using the adaptive mesh-refinement code RAMSES, we computed a first grid of model of star-forming clumps to develop our pipeline and explore the impact of the clump initial conditions on their evolution. The main purpose of this set of simulations is to be converted into synthetic observations to enable a direct comparison with real star-forming clumps observed with Herschel and ALMA. Results. The Rosetta Stone simulations presented here provide a catalog available for full post-processing and subsequent comparison with observations (RS1). Among all the parameters explored here, the strength of the magnetic field has the strongest influence on the clump evolution (fragmentation, star formation, global collapse) at both large and small scales. Numerical parameters such as the resolution per Jeans length or the threshold for accretion onto sink particles affects the formation of low-mass sinks. Finally, the widely used L/M ratio is found to be a good indicator of the clump evolutionary state regardless of its initial condition, but this could change when more feedback processes (jets, HII regions) are included. Conclusions. We now have a new suite of simulations of star-forming clumps that is available for full post-processing and subsequent comparison with the observations,

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