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Abstract 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. 

Abstract Accurately quantifying the impact of radiation feedback in star formation is challenging. To address this complex problem, we employ deep-learning techniques known as denoising diffusion probabilistic models (DDPMs) to predict the interstellar radiation field (ISRF) strength based on three-band dust emission at 4.5, 24, and 250μm. We adopt magnetohydrodynamic simulations from the STARFORGE project that model star formation and giant molecular cloud (GMC) evolution. We generate synthetic dust emission maps matching observed spectral energy distributions in the Monoceros R2 (MonR2) GMC. We train DDPMs to estimate the ISRF using synthetic three-band dust emission. The dispersion between the predictions and true values is within a factor of 0.1 for the test set. We extended our assessment of the diffusion model to include new simulations with varying physical parameters. While there is a consistent offset observed in these out-of-distribution simulations, the model effectively constrains the relative intensity to within a factor of 2. Meanwhile, our analysis reveals a weak correlation between the ISRF solely derived from dust temperature and the actual ISRF. We apply our trained model to predict the ISRF in MonR2, revealing a correspondence between intense ISRF, bright sources, and high dust emission, confirming the model’s ability to capture ISRF variations. Our model robustly predicts radiation feedback distribution, even in complex, poorly constrained ISRF environments like those influenced by nearby star clusters. However, precise ISRF predictions require an accurate training data set mirroring the target molecular cloud’s unique physical conditions. 

ABSTRACT The internal velocity structure within dense gaseous cores plays a crucial role in providing the initial conditions for star formation in molecular clouds. However, the kinematic properties of dense gas at core scales (∼0.01−0.1 pc) has not been extensively characterized because of instrument limitations until the unique capabilities of GBT-Argus became available. The ongoing GBT-Argus Large Program, Dynamics in Star-forming Cores (DiSCo) thus aims to investigate the origin and distribution of angular momentum of star-forming cores. DiSCo will survey all starless cores and Class 0 protostellar cores in the Perseus molecular complex down to ∼0.01 pc scales with <0.05 km s−1 velocity resolution using the dense gas tracer N2H+. Here, we present the first data sets from DiSCo towards the B1 and NGC 1333 regions in Perseus. Our results suggest that a dense core’s internal velocity structure has little correlation with other core-scale properties, indicating these gas motions may be originated externally from cloud-scale turbulence. These first data sets also reaffirm the ability of GBT-Argus for studying dense core velocity structure and provided an empirical basis for future studies that address the angular momentum problem with a statistically broad sample. 

Molecular clouds (MCs) are active sites of star formation in galaxies, and their formation and evolution are largely affected by stellar feedback. This includes outflows and winds from newly formed stars, radiation from young clusters, and supernova explosions. High-resolution molecular line observations allow for the identification of individual star-forming regions and the study of their integrated properties. Moreover, state-of-the-art simulations are now capable of accurately replicating the evolution of MCs, including all key stellar feedback processes. We present¹³CO(2–1) synthetic observations of the STARFORGE simulations produced using the radiative transfer code RADMC-3D, matching the observational setup of the SEDIGISM survey. From these synthetic observations, we identified the population of MCs using hierarchical clustering and analysed them to provide insights into the interpretation of observed MCs as they evolve. The flux distributions of the post-processed synthetic observations and the properties of the MCs, namely, radius, mass, velocity dispersion, virial parameter, and surface density, are consistent with those of SEDIGISM. Both samples of MCs occupy the same regions in the scaling relation plots; however, the average distributions of MCs at different evolutionary stages do not overlap on the plots. This highlights the reliability of our approach in modelling SEDIGISM and suggests that MCs at different evolutionary stages contribute to the scatter in observed scaling relations. We study the trends in MC properties, morphologies, and fragmentation over time to analyse their physical structure as they form, evolve, and are destroyed. MCs appear as small diffuse cloudlets in early stages, and this is followed by their evolution to filamentary structures before being shaped by stellar feedback into 3D bubbles and getting dispersed. These trends in the observable properties of MCs are consistent with other realisations of simulations and provide strong evidence that clouds exhibit distinct morphologies over the course of their evolution. 

Stars form in dense cores within molecular clouds, and newly formed stars influence their natal environments. How stellar feedback impacts core properties and evolution has been the subject of extensive investigation. We performed a hierarchical clustering (dendrogram) analysis of a STARFORGE (STAR FORmation in Gaseous Environments) simulation, modelling a giant molecular cloud to identify gas overdensities (cores) and study changes in their radius, mass, velocity dispersion, and virial parameter with respect to stellar feedback. We binned these cores on the basis of the fraction of gas affected by protostellar outflows, stellar winds, and supernovae and analysed the property distributions for each feedback bin. We find that cores that experience more feedback influence are smaller. Feedback notably enhances the velocity dispersion and virial parameter of the cores, more so than it reduces their radius. This is also evident in the linewidth–size relation, according to which cores in higher-feedback bins exhibit higher velocities than their similarly sized pristine counterparts. We conclude that stellar feedback mechanisms, which impart momentum to the molecular cloud, simultaneously compress and disperse the dense molecular gas.