Clustering Properties of Stars in Simulations of Wind‐driven Star Formation

Scalo, J. M.; Chappell, David W.

doi:10.1086/306567

Cited by 23 publications

(16 citation statements)

References 21 publications

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“…The ÈÀ power-law index is $ À0:84 for the samples with H À K s > 0:7 mag in region D and $ À0:95 in region D1, as indicated by the triple-dot-dashed line segments in Figure 4. The function is steep in comparison both to other regions of the RMC (Li & Smith 2005b, 2005c and to other clusters (e.g., Scalo & Chappell 1999). Hence, clustering is particularly strong here, suggesting that these stars are either tightly bound or extremely young.…”

Section: Spatial Distribution Of Embedded Clusters and Loose Aggregatesmentioning

confidence: 60%

Multiseeded Multimode Formation of Embedded Clusters in the Rosette Molecular Complex: Structured Star Formation toward the Southeastern Boundary

Smith

2005

Astron J

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The Rosette Molecular Complex contains embedded clusters with diverse properties and origins. We have previously explored the shell mode of formation in the north (regions A and B) and the massive concentrations in the ridge (region C). Here we explore star formation toward the south of the complex, region D, based on data from the spatially complete Two Micron All Sky Survey. We find that stars are forming prolifically throughout this region in a highly structured mode, with both clusters and loose aggregates detected. The most prominent cluster (region D1) lies in the north-center. This cluster is over 20 pc to the south of the Monoceros Ridge, the interface of the emerging young OB cluster NGC 2244 with its ambient molecular clouds. In addition, there are several branches stemming from AFGL 961 in region C and extending to the southeastern boundary of the cloud. We invoke a tree model to interpret this pattern, corresponding to probable tracks of abrupt turbulent excitation and subsequent decay. Alternatively, we discuss gravoturbulent collapse scenarios based on numerical simulations. Relative stellar ages and gas flow directions will differentiate between these mechanisms.

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Section: Spatial Distribution Of Embedded Clusters and Loose Aggregatesmentioning

confidence: 60%

Multiseeded Multimode Formation of Embedded Clusters in the Rosette Molecular Complex: Structured Star Formation toward the Southeastern Boundary

Smith

2005

Astron J

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“…The two‐point correlation functions of stars in the simulations are discussed elsewhere (Scalo & Chappell 1999a). Their form is generally power‐law, but the logarithmic slope depends on the age of the stars selected and on the average level of star formation in the models.…”

Section: Results: Spatial Distributionmentioning

confidence: 99%

“…The loci of these star formation regions move throughout the simulation, following complex and 'unpredictable' trajectories. The spatial clustering distribution of the young stars is scale-free, as evidenced by the power law correlation functions presented in Scalo & Chappell (1999a).…”

Section: U Lt I P E R S P E C T I Va L I N T E R P R E Tat I O Nmentioning

confidence: 93%

Wind-driven gas networks and star formation in galaxies: reaction-advection hydrodynamic simulations

Chappell

Scalo

2001

Monthly Notices of the Royal Astronomical Society

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The effects of wind‐driven star formation feedback on the spatio‐temporal organization of stars and gas in galaxies is studied using two‐dimensional intermediate‐representational quasi‐hydrodynamical simulations. The model retains only a reduced subset of the physics, including mass and momentum conservation, fully non‐linear fluid advection, inelastic macroscopic interactions, threshold star formation, and momentum forcing by winds from young star clusters on the surrounding gas. Expanding shells of swept‐up gas evolve through the action of fluid advection to form a ‘turbulent’ network of interacting shell fragments which have the overall appearance of a web of filaments (in two dimensions). A new star cluster is formed whenever the column density through a filament exceeds a critical threshold based on the gravitational instability criterion for an expanding shell, which then generates a new expanding shell after some time delay. A filament‐finding algorithm is developed to locate the potential sites of new star formation. The major result is the dominance of multiple interactions between advectively distorted shells in controlling the gas and star morphology, gas velocity distribution and mass spectrum of high mass density peaks, and the global star formation history. The gas morphology strongly resembles the model envisioned by Norman & Silk, and observations of gas in the Large Magellanic Cloud (LMC) and local molecular clouds. The dependence of the frequency distribution of present‐to‐past average global star formation rate on a number of parameters is investigated. Bursts of star formation only occur when the time‐averaged star formation rate per unit area is low, or the system is small. Percolation does not play a role. The broad distribution observed in late‐type galaxies can be understood as a result of either small size or small metallicity, resulting in larger shell column densities required for gravitational instability. The star formation rate increases with density, but dependences on gas velocity dispersion and average shell column density suggest that the dependence is multivariate. The distribution of gas velocities exhibits exponential tails over a broad range of parameter values and the velocity distribution for gas in filaments is nearly exponential. Decay simulations with no star formation suggest that the exponential tails are caused by multiple shell interactions, not individual stellar winds. The cloud mass spectra, estimated using a simplified version of the structure tree method, tend to be power laws at the higher‐mass end, with an index that is nearly independent of the star formation activity or model parameters. Kinetic energy decay in simulations without star formation yields a t−1 dependence. We discuss how the simulations can be viewed in the context of various incomplete conceptual models, including collisional cloud coalescence, wind‐driven turbulence, propagating star formation, forced mass‐conserving Burgers turbulence, and granular fluids.

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“…The goal of our modeling is not to derive the physical parameters for the SSPSF process; rather it is to demonstrate that a fairly straightforward model grounded in processes that are generally believed to occur in galaxies (dynamical relaxation of stars, correlated star formation, and self-propagation of star formation) can lead to clustering properties that are similar to those observed. More specific inferences will require measurement of the correlation function across the entire LMC and dynamically self-consistent models that have a more detailed, physically motivated prescription for star formation (see Scalo & Chappell 1999 for one such example).…”

Section: The Stochastic Self-propagating Star Formation Modelmentioning

confidence: 99%

On the Spatial Distribution of Stellar Populations in the Large Magellanic Cloud

Harris

Zaritsky

1999

The Astronomical Journal

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We measure the angular correlation function of stars in a region of the Large Magellanic Cloud (LMC) that spans 2.0 • × 1.5 • . We find that the correlation functions of stellar populations are represented well by exponential functions of the angular separation for separations between 2 and 40 arcmin (corresponding to ∼ 30 pc and 550 pc for an LMC distance of 50 kpc). The inner boundary is set by the presence of distinct, highly correlated structures, which are the more familiar stellar clusters, and the outer boundary is set by the observed region's size and the presence of two principal centers of star formation within the region. We also find that the normalization and scale length of the correlation function changes systematically with the mean age of the stellar population. The existence of positive correlation at large separations (∼ 300 pc), even in the youngest population, argues for large-scale hierarchical structure in current star formation. The evolution of the angular correlation toward lower normalizations and longer scale lengths with stellar age argues for the dispersion of stars with time. We show that a simple, stochastic, self-propagating star formation model is qualitatively consistent with this behavior of the correlation function.

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Clustering Properties of Stars in Simulations of Wind‐driven Star Formation

Cited by 23 publications

References 21 publications

Multiseeded Multimode Formation of Embedded Clusters in the Rosette Molecular Complex: Structured Star Formation toward the Southeastern Boundary

Multiseeded Multimode Formation of Embedded Clusters in the Rosette Molecular Complex: Structured Star Formation toward the Southeastern Boundary

Wind-driven gas networks and star formation in galaxies: reaction-advection hydrodynamic simulations

On the Spatial Distribution of Stellar Populations in the Large Magellanic Cloud

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