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This package provides the analysis products and raw simulation snapshots accompanying the paper "Centrally concentrated star formation in young clusters II: Jet feedback." It includes time‑history tables, per‑snapshot stellar particle catalogues, raw FLASH/Torch HDF5 snapshots at two reference epochs, and density‑slice movies for 14 hydrodynamic simulations of star cluster formation with and without protostellar jet feedback, spanning three AMR resolution levels. 

Abstract Stars form in clusters from the gravitational collapse of giant molecular clouds, which is opposed by a variety of physical processes, including stellar feedback. The interplay between these processes determines the star formation rate of the clouds. To study how feedback controls star formation, we use a numerical framework that is optimized to simulate star cluster formation and evolution. This framework, calledTorch, combines the magnetohydrodynamical code FLASH with N-body and stellar evolution codes in the Astrophysical Multipurpose Software Environment (AMUSE).Torchincludes stellar feedback from ionizing and nonionizing radiation, stellar winds, and supernovae, but, until now, did not include protostellar jets. We present our implementation of protostellar jet feedback within theTorchframework and describe its free parameters. We then demonstrate our new module by comparing cluster formation simulations with and without jets. We find that the inclusion of protostellar jets slows star formation, even in clouds of up toM = 2 × 10⁴M_⊙. We also find that the star formation rate of our lower-mass clouds (M = 5 × 10³M_⊙) is strongly affected by both the inclusion of protostellar jets and the chosen jet parameters, including the jet lifetime and injection velocity. We follow the energy budget for each simulation and find that the inclusion of jets systematically increases the kinetic energy of the gas at early times. The implementation of protostellar jet feedback inTorchopens new areas of investigation regarding the role of feedback in star cluster formation and evolution. 

Abstract We use a suite of 3D simulations of star-forming molecular clouds, with and without stellar feedback and magnetic fields, to investigate the effectiveness of different fitting methods for volume and column density probability distribution functions (PDFs). The first method fits a piecewise lognormal and power-law (PL) function to recover PDF parameters such as the PL slope and transition density. The second method fits a polynomial spline function and examines the first and second derivatives of the spline to determine the PL slope and the functional transition density. The first PL (set by the transition between lognormal and PL function) can also be visualized in the derivatives directly. In general, the two methods produce fits that agree reasonably well for volume density but vary for column density, likely due to the increased statistical noise in the column density PDFs as compared to the volume density PDFs. We test a well-known conversion for estimating volume density PL slopes from column density slopes and find that the spline method produces a better match (χ²of 3.34 versusχ²of 5.92), albeit with a significant scatter. Ultimately, we recommend the use of both fitting methods on column density data to mitigate the effects of noise. 

Mass segregation is seen in many star clusters, but whether massive stars form in the center of a cluster or migrate there dynamically is still debated.N-body simulations show that early dynamical mass segregation is possible when sub-clusters merge to form a dense core with a small crossing time. However, the effect of gas dynamics on both the formation and dynamics of the stars could inhibit the formation of the dense core. We aim to study the dynamical mass segregation of star cluster models that include gas dynamics and selfconsistently form stars from the dense substructure in the gas. Our models use the TORCH framework, which is based on AMUSE and includes stellar and magnetized gas dynamics, as well as stellar evolution and feedback from radiation, stellar winds, and supernovae. Our models consist of three star clusters forming from initial turbulent spherical clouds of mass 10⁴, 10⁵, 10⁶M_⊙and radius 11.7 pc that have final stellar masses of 3.6 × 10³M_⊙, 6.5 × 10⁴M_⊙, and 8.9 × 10⁵M_⊙, respectively. There is no primordial mass segregation in the model by construction. All three clusters become dynamically mass segregated at early times via collapse confirming that this mechanism occurs within sub-clusters forming directly out of the dense substructure in the gas. The dynamics of the embedded gas and stellar feedback do not inhibit the collapse of the cluster. We find that each model cluster becomes mass segregated within 2 Myr of the onset of star formation, reaching the levels observed in young clusters in the Milky Way. However, we note that the exact values are highly time-variable during these early phases of evolution. Massive stars that segregate to the center during core collapse are likely to be dynamically ejected, a process that can decrease the overall level of mass segregation again. 

Abstract We present a new framework to incorporate feedback from massive interacting binaries in simulations of star cluster formation. Our new feedback model adds binary stellar evolution to the cluster formation codeTorch, and couples it inAmuseto the preexisting modules for collisional stellar dynamics, magnetohydrodynamics, and mechanical and radiative feedback. Our model accounts for the effects of mass transfer on the stars’ mass-loss rates, their radiation spectra, and the timing of core-collapse supernovae. It also injects mass lost through nonconservative mass transfer and CE ejection into the interstellar medium (ISM). We demonstrate the use of our feedback model through simulations of isolated binaries in a gaseous medium, and of embedded clusters of massive binaries. Feedback from interacting binaries efficiently couples with the surrounding ISM. It increases the size of Hiiregions, increases the kinetic and thermal energy of the gas, and increases the pressure within Hiiregions compared to models that use single star stellar evolution. Those differences arise from the ionizing radiation, which increases by 3 orders of magnitude, resulting in Hiiregions that expand due to thermal pressure rather than radiation pressure. The effects of stellar dynamics and the gravitational potential of the background gas cause the evolution of individual binaries to deviate from the predictions made by secular evolution, impacting the subsequent feedback from the binary. We conclude that massive interacting binaries are an important source of feedback in cluster-forming regions, and must be considered when studying the emerging timescales of young star clusters. 

Abstract We study the evolution of populations of binary stars within massive cluster-forming regions. We simulate the formation of young massive star clusters within giant molecular clouds with masses ranging from 2 × 10⁴to 3.2 × 10⁵M_⊙. We use Torch, which couples stellar dynamics, magnetohydrodynamics, star and binary formation, stellar evolution, and stellar feedback through the Amuseframework. We find that the binary fraction decreases during cluster formation at all molecular cloud masses. The binaries’ orbital properties also change, with stronger and quicker changes in denser, more massive clouds. Most of the changes we see can be attributed to the disruption of binaries wider than 100 au, although the close binary fraction also decreases in the densest cluster-forming region. The binary fraction for O stars remains above 90%, but exchanges and dynamical hardening are ubiquitous, indicating that O stars undergo frequent few-body interactions early during the cluster formation process. Changes to the populations of binaries are a by-product of hierarchical cluster assembly: most changes to the binary population take place when the star formation rate is high, and there are frequent mergers between subclusters in the cluster-forming region. A universal primordial binary distribution based on observed inner companions in the Galactic field is consistent with the binary populations of young clusters with resolved stellar populations, and the scatter between clusters of similar masses could be explained by differences in their formation history. 

Two main mechanisms have classically been proposed for the formation of runaway stars. In the binary supernova scenario (BSS), a massive star in a binary explodes as a supernova, ejecting its companion. In the dynamical ejection scenario, a star is ejected during a strong dynamical encounter between multiple stars. We propose a third mechanism for the formation of runaway stars: the subcluster ejection scenario (SCES), where a subset of stars from an infalling subcluster is ejected out of the cluster via a tidal interaction with the contracting gravitational potential of the assembling cluster. We demonstrate the SCES in a star-by-star simulation of the formation of a young massive cluster from a 10⁶M_⊙gas cloud using theTORCHframework. This star cluster forms hierarchically through a sequence of subcluster mergers determined by the initial turbulent, spherical conditions of the gas. We find that these mergers drive the formation of runaway stars in our model. Late-forming subclusters fall into the central potential, where they are tidally disrupted, forming tidal tails of runaway stars that are distributed highly anisotropically. Runaways formed in the same SCES have similar ages, velocities, and ejection directions. Surveying observations, we identify several SCES candidate groups with anisotropic ejection directions. The SCES is capable of producing runaway binaries: two wide dynamical binaries in infalling subclusters were tightened through ejection. This allows for another velocity kick via subsequent via a subsequent BSS ejection. An SCES-BSS ejection is a possible avenue for the creation of hypervelocity stars unbound to the Galaxy. The SCES occurs when subcluster formation is resolved. We expect nonspherical initial gas distributions to increase the number of calculated runaway stars, bringing it closer to observed values. The observation of groups of runaway stars formed via the SCES can thus reveal the assembly history of their natal clusters. 

The mode of star formation that results in the formation of globular clusters and young massive clusters is difficult to constrain through observations. We present models of massive star cluster formation using the TORCHframework, which uses the Astrophysical MUltipurpose Software Environment (AMUSE) to couple distinct multi-physics codes that handle star formation, stellar evolution and dynamics, radiative transfer, and magnetohydrodynamics. We upgraded TORCHby implementing the N-body code PETAR, thereby enabling TORCHto handle massive clusters forming from 10⁶M_⊙clouds with ≥10⁵individual stars. We present results from TORCHsimulations of star clusters forming from 10⁴,  10⁵, and 10⁶M_⊙turbulent spherical gas clouds (named M4, M5, M6) of radiusR= 11.7 pc. We find that star formation is highly efficient and becomes more so at a higher cloud mass and surface density. For M4, M5, and M6 with initial surface densities 2.325 × 10^1,2,3M_⊙pc⁻², after a free-fall time oft_ff= 6.7,2.1,0.67 Myr, we find that ∼30%, 40%, and 60% of the cloud mass has formed into stars, respectively. The end of simulation-integrated star formation efficiencies for M4, M5, and M6 areϵ_⋆ = M_⋆/M_cloud = 36%, 65%, and 85%. Observations of nearby clusters similar in mass and size to M4 have instantaneous star formation efficiencies ofϵ_inst ≤ 30%, which is slightly lower than the integrated star formation efficiency of M4. The M5 and M6 models represent a different regime of cluster formation that is more appropriate for the conditions in starburst galaxies and gas-rich galaxies at high redshift, and that leads to a significantly higher efficiency of star formation. We argue that young massive clusters build up through short efficient bursts of star formation in regions that are sufficiently dense (Σ ≥ 10²M_⊙pc⁻²) and massive (M_cloud≥ 10⁵M_⊙). In such environments, stellar feedback from winds and radiation is not strong enough to counteract the gravity from gas and stars until a majority of the gas has formed into stars. 

Abstract We use a suite of 3D simulations of star-forming molecular clouds, with and without stellar feedback, magnetic fields, and driven turbulence, to study the compression and expansion rates of the gas as functions of density. We show that, around the mean density, supersonic turbulence promotes rough equilibrium between the amounts of compressing and expanding gas, consistent with continuous gas cycling between high- and low-density states. We find that the inclusion of protostellar jets produces rapidly expanding and compressing low-density gas. We find that the gas mass flux peaks at the transition between the lognormal and power-law forms of the density probability distribution function (PDF). This is consistent with the transition density tracking the post-shock density, which promotes an enhancement of mass at this density (i.e., shock compression and filament formation). At high densities, the gas dynamics are dominated by self-gravity: the compression rate in all of our runs matches the rate of the run with only gravity, suggesting that processes other than self-gravity have little effect at these densities. The net gas mass flux becomes constant at a density below the sink formation threshold, where it equals the star formation rate. The density at which the net gas mass flux equals the star formation rate is one order of magnitude lower than our sink threshold density, corresponds to the formation of the second power-law tail in the density PDF, and sets the overall star formation rates of these simulations.