Explore the vast range of titles available.

The star formation efficiency per free-fall time (ϵff) quantifies how efficiently giant molecular clouds (GMCs) convert gas into stars and is often observed to be orders of magnitude lower than expected for free-fall collapse. Observers typically estimate ϵff by mapping far-infrared (FIR) dust emission to infer gas surface densities (Σgas) and counting embedded protostars, assuming simplified lifetimes and masses. Using the fiducial starforge radiation–magnetohydrodynamics simulation of an Mcl = 2 × 104 M⊙ GMC that self-consistently models star cluster formation and stellar feedback, we generate synthetic FIR observations and apply the same methodologies used in GMC surveys to investigate this discrepancy. We present the first ϵff − Σgas analysis in a fully feedback-regulated GMC simulation that resolves the formation of stellar systems. Our synthetic measurements reproduce the low observationally inferred efficiencies, showing that feedback-regulated star formation naturally produces ϵff ∼ 1%–3% without requiring extreme initial conditions. We also find that ϵff varies strongly over a GMC’s lifetime, suggesting that much of the observed scatter reflects evolutionary sampling rather than intrinsic cloud-to-cloud differences. Finally, by comparing observational and simulation definitions, we show that methodological assumptions introduce systematic biases, with a transition near logΣgas≈2.3M⊙pc−2 that depends on resolution. Above this threshold, the smoothing of dense structure increases both the inferred free-fall time and enclosed gas, with the latter dominating and suppressing ϵff. Below the threshold, the discrepancies are primarily driven by star formation rate assumptions.

The dust grain size distribution (GSD) likely varies significantly across star-forming environments in the Universe, but its impact on star formation remains unclear. This ambiguity arises because the GSD interacts nonlinearly with processes like heating, cooling, radiation, and chemistry, which have competing effects and varying environmental dependencies. Processes such as grain coagulation, expected to be efficient in dense star-forming regions, reduce the abundance of small grains and increase that of larger grains. Motivated by this, we investigate the effects of similar GSD variations on the thermochemistry and evolution of giant molecular clouds (GMCs) using magnetohydrodynamic simulations spanning a range of cloud masses and grain sizes, which explicitly incorporate the dynamics of dust grains within the full-physics framework of the STARFORGE project. We find that grain size variations significantly alter GMC thermochemistry: the leading-order effect is that larger grains, under fixed dust mass, GSD dynamic range, and dust-to-gas ratio, result in lower dust opacities. This reduced opacity permits interstellar radiation field and internal radiation photons to penetrate more deeply. This leads to rapid gas heating and inhibited star formation. Star formation efficiency is highly sensitive to grain size, with an order-of-magnitude reduction when grain size dynamic range increases from 10−3–0.1 μm to 0.1–10 μm. Additionally, warmer gas suppresses low-mass star formation, and decreased opacities result in a greater proportion of gas in diffuse ionized structures.

Stars form within dense cores composed of both gas and dust within molecular clouds. However, despite the crucial role that dust plays in the star formation process, its dynamics is frequently overlooked, with the common assumption being a constant, spatially uniform dust-to-gas ratio and grain size spectrum. In this study, we introduce a set of radiation-dust-magnetohydrodynamic simulations of star-forming molecular clouds from the STARFORGE project. These simulations expand upon the earlier radiation MHD models, which included cooling, individual star formation, and feedback. Notably, they explicitly address the dynamics of dust grains, considering radiation, drag, and Lorentz forces acting on a diverse size spectrum of live dust grains. We find that once stars exceed a certain mass threshold (∼2 M ⊙), their emitted radiation can evacuate dust grains from their vicinity, giving rise to a dust-suppressed zone of size ∼100 au. This removal of dust, which interacts with gas through cooling, chemistry, drag, and radiative transfer, alters the gas properties in the region. Commencing during the early accretion stages and preceding the main-sequence phase, this process results in a mass-dependent depletion in the accreted dust-to-gas (ADG) mass ratio within both the circumstellar disk and the star. We predict that massive stars (≳10 M ⊙) would exhibit ADG ratios that are approximately 1 order of magnitude lower than that of their parent clouds. Consequently, stars, their disks, and circumstellar environments would display notable deviations in the abundances of elements commonly associated with dust grains, such as carbon and oxygen.

Star formation occurs within dense regions of giant molecular clouds (GMCs); however, exactly how gas collects and evolves to form individual stars and what role dense cores play remains unclear. We use the Lagrangian cell information in the STARFORGE simulation suite to track star-forming gas in three GMCs with varying magnetic field strengths. We find that once a protostar forms, the lifetime of the unaccreted gas correlates with the final stellar mass, where low-mass stars (M* < 0.5M⊙) accrete for 0.5–0.6 Myr from a relatively local reservoir of gas and high-mass stars (M* > 2M⊙) accrete over 3.3–4.7 Myr from a much larger volume. Although the protostellar accretion time increases weakly with magnetic field strength, the accreting gas radii, velocity dispersions, virial parameters, and magnetic energy ratios are largely insensitive to the global cloud properties. At the time of protostar formation, the unaccreted gas exhibits linewidth-size and mass-size relations characteristic of turbulently regulated, isothermal dense cores, following σv ∝ R0.47−0.55 and M ∝ R1.0−1.1, respectively. Low- and intermediate-mass stars undergo relatively continuous accretion, and their accretion histories are well-fit by isothermal sphere, turbulent core, or competitive accretion models, where no one model fits all masses. However, many high-mass stars experience intermittent accretion, and their accretion histories are not well-fit by any of these models. While the distribution of accreting gas is more extended than typically defined dense cores, the physical properties and structure of the star-forming gas resemble those of observed cores and are largely regulated by turbulence and feedback.

Cosmic rays (CRs) are the primary driver of ionization in star-forming molecular clouds (MCs). Despite their potential impacts on gas dynamics and chemistry, no simulations of star cluster formation following the creation of individual stars have included explicit cosmic-ray transport (CRT) to date. We conduct the first numerical simulations following the collapse of a 2000M ⊙ MC and the subsequent star formation including CRT using the STAR FORmation in Gaseous Environments framework implemented in the GIZMO code. We show that when CRT is streaming-dominated, the CR energy in the cloud is strongly attenuated due to energy losses from the streaming instability. Consequently, in a Milky Way–like environment the median CR ionization rate in the cloud is low (ζ ≲ 2 × 10−19 s−1) during the main star-forming epoch of the calculation and the impact of CRs on the star formation in the cloud is limited. However, in high-CR environments, the CR distribution in the cloud is elevated (ζ ≲ 6 × 10−18), and the relatively higher CR pressure outside the cloud causes slightly earlier cloud collapse and increases the star formation efficiency by 50% to ∼13%. The initial mass function is similar in all cases except with possible variations in a high-CR environment. Further studies are needed to explain the range of ionization rates observed in MCs and explore star formation in extreme CR environments.

Observations in the Galaxy and nearby spirals have established that the H i-to-H2 transition at solar metallicity occurs at gas weight of PDE/kB ≈ 104 K cm−3, similar to solar neighborhood conditions. Even so, state-of-the-art models of a self-regulated interstellar medium (ISM) underproduce the molecular fraction ( Rmol≡MH2/MHI ) at solar neighborhood conditions by a factor of ≈2–4. We use the GHOSDT suite of simulations at a mass resolution range of 100–0.25 M⊙(effective spatial resolution range of ∼20–0.05 pc) run for 500 Myr to show how this problem is affected by modeling choices such as the inclusion of photoionizing radiation, assumed supernova energy, numerical resolution, inclusion of magnetic fields, and including a model for subgrid clumping. We find that Rmol is not converged even at a resolution of 1 M⊙, with Rmol increasing by a factor of 2 when resolution is improved from 10 to 1 M⊙. Models excluding either photoionization or magnetic fields result in a factor 2 reduction in Rmol. The only model that agrees with the observed value of Rmol includes our subgrid clumping model, which enhances Rmol by a factor of ∼3 compared with our fiducial model. This increases the time-averaged Rmol to 0.25, in agreement with the solar circle value, and closer to the observed median value of 0.42 in regions comparable to the solar neighborhood in nearby spirals. Our findings show that small-scale clumping in the ISM plays a significant role in H2 formation even in high-resolution numerical simulations.

Stellar feedback influences the star formation rate (SFR) and the interstellar medium of galaxies in ways that are difficult to quantify numerically, because feedback is an essential ingredient of realistic simulations. To overcome this, we conduct a feedback-halting experiment starting with a Milky Way–mass galaxy in the second-generation Feedback In Realistic Environments (FIRE-2) simulation framework. By terminating feedback, and comparing to a simulation in which feedback is maintained, we monitor how the runs diverge. We find that without feedback, the interstellar turbulent velocities decay. There is a marked increase of dense material, while the SFR increases by over an order of magnitude. Importantly, this SFR boost is a factor of ∼15–20 larger than is accounted for by the increased freefall rate caused by higher densities. This implies that feedback moderates the star formation efficiency per freefall time more directly than simply through the density distribution. To probe changes at the scale of giant molecular clouds (GMCs), we identify GMCs using density and virial parameter thresholds, tracking clouds as the galaxy evolves. Halting feedback stimulates rapid changes, including a proliferation of new bound clouds, a decrease of turbulent support in loosely bound clouds, an overall increase in cloud densities, and a surge of internal star formation. Computing the cloud-integrated SFR using several theories of turbulence regulation, we show that these theories underpredict the surge in SFR by at least a factor of 3. We conclude that galactic star formation is essentially feedback regulated on scales that include GMCs, and that stellar feedback affects GMCs in multiple ways.

Barnard’s Loop is a famous arc of Hα emission located in the Orion star-forming region. Here, we provide evidence of a possible formation mechanism for Barnard’s Loop and compare our results with recent work suggesting a major feedback event occurred in the region around 6 Myr ago. We present a 3D model of the large-scale Orion region, indicating coherent, radial, 3D expansion of the OBP-Near/Briceño-1 (OBP-B1) cluster in the middle of a large dust cavity. The large-scale gas in the region also appears to be expanding from a central point, originally proposed to be Orion X. OBP-B1 appears to serve as another possible center, and we evaluate whether Orion X or OBP-B1 is more likely to have caused the expansion. We find that neither cluster served as the single expansion center, but rather a combination of feedback from both likely propelled the expansion. Recent 3D dust maps are used to characterize the 3D topology of the entire region, which shows Barnard’s Loop’s correspondence with a large dust cavity around the OPB-B1 cluster. The molecular clouds Orion A, Orion B, and Orion λ reside on the shell of this cavity. Simple estimates of gravitational effects from both stars and gas indicate that the expansion of this asymmetric cavity likely induced anisotropy in the kinematics of OBP-B1. We conclude that feedback from OBP-B1 has affected the structure of the Orion A, Orion B, and Orion λ molecular clouds and may have played a major role in the formation of Barnard’s Loop.

We explore the relation between stellar surface density and gas surface density (the star–gas, or S-G, correlation) in a 20,000 M ⊙ 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 (ΣYSO/Σgas) 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.

Zeeman observations provide the only direct probe of line-of-sight (LOS) magnetic fields in the interstellar medium. To evaluate their accuracy and limitations, we generate synthetic H I Zeeman spectra from magnetohydrodynamic simulations and idealized cloud models, and analyze the resulting Stokes I and V profiles using two complementary methods. Approach I uses the classical relation between Stokes V and dI/dν to estimate LOS-averaged magnetic fields, achieving an upper-limit relative error of ∼16% (half-width of 68.27% confidence interval) for a representative noise level of 0.014 K. Approach II applies Gaussian decomposition to Stokes I and V to estimate component-level magnetic fields, yielding a ∼13% relative error quantifying the same confidence range, reflecting the intrinsic uncertainty of such Zeeman estimates. Both approaches recover the original fields under uniform-field conditions and remain robust in turbulent environments. Approach I provides a simple and reliable LOS-averaged field estimate, while Approach II, although more complex, offers statistical insight into magnetic field variations along the LOS. We further show that joint fitting of Stokes I and V generally outperforms sequential fitting, particularly in the presence of attenuation. Increasing noise eightfold produces a more modest rise in uncertainty, doubling to a ∼26% relative error, while substantial optical depth introduces only a minor additional contribution to the overall uncertainty. Applying these methods to FAST observations of the L1544 star-forming region, we confirm the previously reported LOS magnetic field strength, demonstrating the validity of Zeeman analysis in this benchmark core.