Effects of the environment on the multiplicity properties of stars in the STARFORGE simulations

Abstract

Most observed stars are part of a multiple star system, but the formation of such systems and the role of environment and various physical processes is still poorly understood. We present a suite of radiation-magnetohydrodynamic simulations of star-forming molecular clouds from the STARFORGE project that include stellar feedback with varied initial surface density, magnetic fields, level of turbulence, metallicity, interstellar radiation field, simulation geometry and turbulent driving. In our fiducial cloud the raw simulation data reproduces the observed multiplicity fractions for Solar-type and higher mass stars, similar to previous works. However, after correcting for observational incompleteness the simulation under-predicts these values. The discrepancy is likely due to the lack of disk fragmentation, as the simulation only resolves multiples that form either through capture or core fragmentation. The raw mass distribution of companions is consistent with randomly drawing from the initial mass function for the companions of > 1 M stars, however, accounting for observational incompleteness produces a flatter distribution similar to observations. We show that stellar multiplicity changes as the cloud evolves and anti-correlates with stellar density. This relationship also explains most multiplicity variations between runs, i.e., variations in the initial conditions that increase stellar density (increased surface density, reduced turbulence) decrease multiplicity. While other parameters, such as metallicity, interstellar radiation, and geometry significantly affect the star formation history or the IMF, varying them produces no clear trend in stellar multiplicity properties.

Unbound stars hold the key to young star cluster history

“…These processes influence the type and frequency of the formed planetary systems. Another example is binary capture and destruction processes which can alter the binary fraction in clusters (Kaczmarek et al 2011;Marks et al 2014;Guszejnov et al 2022).…”