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We explore the relation between stellar surface density and gas surface density (the star–gas, or S-G, correlation) in a 20,000M_⊙simulation from the STAR FORmation in Gaseous Environments (starforge) project. We create synthetic observations based on the Spitzer and Herschel telescopes by modeling contamination by active galactic nuclei, smoothing based on angular resolution, cropping the field of view, and removing close neighbors and low-mass sources. We extract S-G properties such as the dense gas-mass fraction, the Class II:I ratio, and the S-G correlation (Σ_YSO/Σ_gas) from the simulation and compare them to observations of giant molecular clouds, young clusters, and star-forming regions, as well as to analytical models. We find that the simulation reproduces trends in the counts of young stellar objects and the median slope of the S-G correlation. This implies that the S-G correlation is not simply the result of observational biases, but is in fact a real effect. However, other statistics, such as the Class II:I ratio and dense gas-mass fraction, do not always match observed equivalents in nearby clouds. This motivates further observations covering the full simulation age range and more realistic modeling of cloud formation.

 

Most stars form in highly clustered environments within molecular clouds, but eventually disperse into the distributed stellar field population. Exactly how the stellar distribution evolves from the embedded stage into gas-free associations and (bound) clusters is poorly understood. We investigate the long-term evolution of stars formed in the starforge simulation suite – a set of radiation-magnetohydrodynamic simulations of star-forming turbulent clouds that include all key stellar feedback processes inherent to star formation. We use nbody6++gpu to follow the evolution of the young stellar systems after gas removal. We use HDBSCAN to define stellar groups and analyse the stellar kinematics to identify the true bound star clusters. The conditions modeled by the simulations, i.e. global cloud surface densities below 0.15 g cm−2, star formation efficiencies below 15 per cent, and gas expulsion time-scales shorter than a free fall time, primarily produce expanding stellar associations and small clusters. The largest star clusters, which have ∼1000 bound members, form in the densest and lowest velocity dispersion clouds, representing ∼32 and 39 per cent of the stars in the simulations, respectively. The cloud’s early dynamical state plays a significant role in setting the classical star formation efficiency versus bound fraction relation. All stellar groups follow a narrow mass-velocity dispersion power-law relation at 10 Myr with a power-law index of 0.21. This correlation result in a distinct mass–size relationship for bound clusters. We also provide valuable constraints on the gas dispersal time-scale during the star formation process and analyse the implications for the formation of bound systems.

 

Abstract We define a sample of 200 protostellar outflows showing blue- and redshifted CO emission in the nearby molecular clouds Ophiuchus, Taurus, Perseus, and Orion, to investigate the correlation between outflow orientations and local, but relatively large-scale, magnetic field directions traced by Planck 353 GHz dust polarization. At high significance ( p ∼ 10 −4 ), we exclude a random distribution of relative orientations and find that there is a preference for alignment of projected plane of sky outflow axes with magnetic field directions. The distribution of relative position angles peaks at ∼30° and exhibits a broad dispersion of ∼50°. These results indicate that magnetic fields have dynamical influence in regulating the launching and/or propagation directions of outflows. However, the significant dispersion around perfect alignment orientation implies that there are large measurement uncertainties and/or a high degree of intrinsic variation caused by other physical processes, such as turbulence or strong stellar dynamical interactions. Outflow to magnetic field alignment is expected to lead to a correlation in the directions of nearby outflow pairs, depending on the degree of order of the field. Analyzing this effect, we find limited correlation, except on relatively small scales ≲0.5 pc. Furthermore, we train a convolutional neural network to infer the inclination angle of outflows with respect to the line of sight and apply it to our outflow sample to estimate their full 3D orientations. We find that the angles between outflow pairs in 3D space also show evidence of small-scale alignment. 

ABSTRACT We study the formation, evolution, and collapse of dense cores by tracking structures in a magnetohydrodynamic simulation of a star-forming cloud. We identify cores using the dendrogram algorithm and utilize machine learning techniques, including Neural Gas prototype learning and Fuzzy c-means clustering to analyse the density and velocity dispersion profiles of cores together with six bulk properties. We produce a 2-d visualization using a Uniform Manifold Approximation and Projection (UMAP), which facilitates the connection between physical properties and three partially-overlapping phases: i) unbound turbulent structures (Phase I), ii) coherent cores that have low turbulence (Phase II), and iii) bound cores, many of which become protostellar (Phase III). Within Phase II, we identify a population of long-lived coherent cores that reach a quasi-equilibrium state. Most prestellar cores form in Phase II and become protostellar after evolving into Phase III. Due to the turbulent cloud environment, the initial core properties do not uniquely predict the eventual evolution, i.e. core evolution is stochastic, and cores follow no one evolutionary path. The phase lifetimes are 1.0 ± 0.1 × 105 yr, 1.3 ± 0.2 × 105 yr, and 1.8 ± 0.3 × 105 yr for Phase I, II, and III, respectively. We compare our results to NH3 observations of dense cores. Known coherent cores predominantly map into Phase II, while most turbulent pressure-confined cores map to Phase I or III. We predict that a significant fraction of observed starless cores have unresolved coherent regions and that ≳20 per cent of observed starless cores will not form stars. Measurements of core radial profiles in addition to the usual bulk properties will enable more accurate predictions of core evolution. 

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 underpredicts these values. The discrepancy is likely due to the lack of disc 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 $\gt 1\, \mathrm{M}_{\rm \odot }$ stars. However, accounting for observational incompleteness produces a flatter distribution similar to observations. We show that stellar multiplicity changes as the cloud evolves and anticorrelates 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) also act to 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.

 

Abstract We adopt the deep learning method casi-3d (Convolutional Approach to Structure Identification-3D) to systemically identify protostellar outflows in 12 CO and 13 CO observations of the nearby molecular clouds, Ophiuchus, Taurus, Perseus, and Orion. The total outflow masses are 267 M ⊙ , 795 M ⊙ , 1305 M ⊙ , and 6332 M ⊙ for Ophiuchus, Taurus, Perseus, and Orion, respectively. We show the outflow mass in each cloud is linearly proportional to the total number of young stellar objects. The estimated total 3D deprojected outflow energies are 9 × 10 45 erg, 6 × 10 46 erg, 1.2 × 10 47 erg, and 6 × 10 47 erg for Ophiuchus, Taurus, Perseus, and Orion, respectively. The energy associated with outflows is sufficient to offset turbulent dissipation at the current epoch for all four clouds. All clouds also exhibit a break point in the spatial power spectrum of the outflow prediction map, which likely corresponds to the typical outflow mass and energy injection scale. 

One of the key mysteries of star formation is the origin of the stellar initial mass function (IMF). The IMF is observed to be nearly universal in the Milky Way and its satellites, and significant variations are only inferred in extreme environments, such as the cores of massive elliptical galaxies and the Central Molecular Zone. In this work, we present simulations from the STARFORGE project that are the first cloud-scale radiation-magnetohydrodynamic simulations that follow individual stars and include all relevant physical processes. The simulations include detailed gas thermodynamics, as well as stellar feedback in the form of protostellar jets, stellar radiation, winds, and supernovae. In this work, we focus on how stellar radiation, winds, and supernovae impact star-forming clouds. Radiative feedback plays a major role in quenching star formation and disrupting the cloud; however, the IMF peak is predominantly set by protostellar jet physics. We find that the effect of stellar winds is minor, and supernovae ‘occur too late’ to affect the IMF or quench star formation. We also investigate the effects of initial conditions on the IMF. We find that the IMF is insensitive to the initial turbulence, cloud mass, and cloud surface density, even though these parameters significantly shape the star formation history of the cloud, including the final star formation efficiency. Meanwhile, the characteristic stellar mass depends weakly on metallicity and the interstellar radiation field, which essentially set the average gas temperature. Finally, while turbulent driving and the level of magnetization strongly influence the star formation history, they only influence the high-mass slope of the IMF.

 

Abstract One of the most poorly understood aspects of low-mass star formation is how multiple-star systems are formed. Here we present the results of Atacama Large Millimeter/submillimeter Array (ALMA) Band 6 observations toward a forming quadruple protostellar system, G206.93-16.61E2, in the Orion B molecular cloud. ALMA 1.3 mm continuum emission reveals four compact objects, of which two are Class I young stellar objects and the other two are likely in prestellar phase. The 1.3 mm continuum emission also shows three asymmetric ribbon-like structures that are connected to the four objects, with lengths ranging from ∼500 to ∼2200 au. By comparing our data with magnetohydrodynamic simulations, we suggest that these ribbons trace accretion flows and also function as gas bridges connecting the member protostars. Additionally, ALMA CO J = 2−1 line emission reveals a complicated molecular outflow associated with G206.93-16.61E2, with arc-like structures suggestive of an outflow cavity viewed pole-on. 

Stars form in dense, clustered environments, where feedback from newly formed stars eventually ejects the gas, terminating star formation and leaving behind one or more star clusters. Using the STARFORGE simulations, it is possible to simulate this process in its entirety within a molecular cloud, while explicitly evolving the gas radiation and magnetic fields and following the formation of individual, low-mass stars. We find that individual star-formation sites merge to form ever larger structures, while still accreting gas. Thus clusters are assembled through a series of mergers. During the cluster assembly process, a small fraction of stars are ejected from their clusters; we find no significant difference between the mass distribution of the ejected stellar population and that of stars inside clusters. The star-formation sites that are the building blocks of clusters start out mass segregated with one or a few massive stars at their centre. As they merge the newly formed clusters maintain this feature, causing them to have mass-segregated substructures without themselves being centrally condensed. The merged clusters relax to a centrally condensed mass-segregated configuration through dynamical interactions between their members, but this process does not finish before feedback expels the remaining gas from the cluster. In the simulated runs, the gas-free clusters then become unbound and breakup. We find that turbulent driving and a periodic cloud geometry can significantly reduce clustering and prevent gas expulsion. Meanwhile, the initial surface density and level of turbulence have little qualitative effect on cluster evolution, despite the significantly different star formation histories.