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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. 

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. 

Abstract We present a Spitzer/Herschel focused survey of the Aquila molecular clouds ( d ∼ 436 pc) as part of the eHOPS (extension of the Herschel orion protostar survey, or HOPS, Out to 500 ParSecs) census of nearby protostars. For every source detected in the Herschel/PACS bands, the eHOPS-Aquila catalog contains 1–850 μ m SEDs assembled from the Two Micron All Sky Survey, Spitzer, Herschel, the Wide-field Infrared Survey Explorer, and James Clerk Maxwell Telescope/SCUBA-2 data. Using a newly developed set of criteria, we classify objects by their SEDs as protostars, pre-main-sequence stars with disks, and galaxies. A total of 172 protostars are found in Aquila, tightly concentrated in the molecular filaments that thread the clouds. Of these, 71 (42%) are Class 0 protostars, 54 (31%) are Class I protostars, 43 (25%) are flat-spectrum protostars, and four (2%) are Class II sources. Ten of the Class 0 protostars are young PACS bright red sources similar to those discovered in Orion. We compare the SEDs to a grid of radiative transfer models to constrain the luminosities, envelope densities, and envelope masses of the protostars. A comparison of the eHOPS-Aquila to the HOPS protostars in Orion finds that the protostellar luminosity functions in the two star-forming regions are statistically indistinguishable, the bolometric temperatures/envelope masses of eHOPS-Aquila protostars are shifted to cooler temperatures/higher masses, and the eHOPS-Aquila protostars do not show the decline in luminosity with evolution found in Orion. We briefly discuss whether these differences are due to biases between the samples, diverging star formation histories, or the influence of environment on protostellar evolution. 

Abstract We review the use of young low mass stars and protostars, or young stellar objects (YSOs), as tracers of star formation. Observations of molecular clouds at visible, infrared, radio and X-ray wavelengths can identify and characterize the YSOs populating these clouds, with the ability to detect deeply embedded objects at all evolutionary stages. Surveys with the Spitzer, Herschel, XMM-Newton and Chandra space telescopes have measured the spatial distribution of YSOs within a number of nearby (<2.5 kpc) molecular clouds, showing surface densities varying by more than three orders of magnitude. These surveys have been used to measure the spatially varying star formation rates and efficiencies within clouds, and when combined with maps of the molecular gas, have led to the discovery of star-forming relations within clouds. YSO surveys can also characterize the structures, ages, and star formation histories of embedded clusters, and they illuminate the relationship of the clusters to the networks of filaments, hubs and ridges in the molecular clouds from which they form. Measurements of the proper motions and radial velocities of YSOs trace the evolving kinematics of clusters from the deeply embedded phases through gas dispersal, providing insights into the factors that shape the formation of bound clusters. On 100 pc scales that encompass entire star-forming complexes, Gaia is mapping the young associations of stars that have dispersed their natal gas and exist alongside molecular clouds. These surveys reveal the complex structures and motions in associations, and show evidence for supernova driven expansions. Remnants of these associations have now been identified by Gaia, showing that traces of star-forming structures can persist for a few hundred million years.