Influence of magnetic field and stellar radiative feedback on the collapse and the stellar mass spectrum of a massive star-forming clump

Hennebelle, P.; Lebreuilly, Ugo; Colman, Tine; Elia, Davide; Fuller, G. A.; Leurini, S.; Nony, T.; Schisano, E.; Soler, J. D.; Traficante, A.; Klessen, Ralf S.; Molinari, S.; Testi, L.

doi:10.1051/0004-6361/202243803

Cited by 11 publications

(2 citation statements)

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“…Yet, their expansion locally compresses the gas, enhancing its density and directly impacting the properties of newly forming clumps (Zhang et al 2020(Zhang et al , 2021. These events can affect scales from tens of parsecs down to sub-parsec scales, possibly altering the local star formation properties (Palmeirim et al 2017;Mazumdar et al 2021), or even promoting the formation of other massive stars by inhibiting gas fragmentation (Hennebelle et al 2022).…”

Section: Introductionmentioning

confidence: 99%

Stellar feedback in the star formation–gas density relation: Comparison between simulations and observations

Suin,

Zavagno,

Colman

et al. 2024

A&A

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The impact of stellar feedback on the Kennicutt-Schmidt (KS) law, which relates the star formation rate (SFR) to the surface gas density, is a topic of ongoing debate. The interpretation of high-resolution observations of individual clouds is challenging due to the various processes at play simultaneously and inherent biases. Therefore, a numerical investigation is necessary to understand the role of stellar feedback and identify observable signatures. In this study we investigate the impact of stellar feedback on the KS law, aiming to identify distinct signatures that can be observed and analysed. By employing magnetohydrodynamic simulations of an isolated cloud, we specifically isolate the effects of high-mass star radiation feedback and protostellar jets. High-resolution numerical simulations are a valuable tool for isolating the impact of stellar feedback on the star formation process, while also allowing us to assess how observational biases may affect the derived relation. We used high-resolution (<0.01 pc) magnetohydrodynamic numerical simulations of a 10$^4\ cloud and followed its evolution under different feedback prescriptions. The set of simulations contained four types of feedback: one with only protostellar jets, one with ionising radiation from massive stars (>8 odot $), one with the combination of the two, and one without any stellar feedback. In order to compare these simulations with the existing observational results, we analysed their evolution by adopting the same techniques applied in the observational studies. Then, we simulated how the same analyses would change if the data were affected by typical observational biases: counting young stellar objects (YSO) to estimate the SFR, the limited resolution for the column density maps, and a sensitivity threshold for detecting faint embedded YSOs. Our analysis reveals that the presence of stellar feedback strongly influences the shape of the KS relation and the star formation efficiency per free-fall time (eff ). The impact of feedback on the relation is primarily governed by its influence on the cloud's structure. Additionally, the evolution of eff throughout the star formation event suggests that variations in this quantity can mask the impact of feedback in observational studies that do not account for the evolutionary stage of the clouds. Although the eff measured in our clouds is higher than what is usually observed in real clouds, upon applying prescriptions to mimic observational biases we recover a good agreement with the expected values. From that, we can infer that observations tend to underestimate the total SFR. Moreover, this likely indicates that the physics included in our simulations is sufficient to reproduce the basic mechanisms that contribute to setting eff . We demonstrate the interest of employing numerical simulations to address the impact of early feedback on star formation laws and to correctly interpret observational data. This study will be extended to other types of molecular clouds and ionising stars, sampling different feedback strengths, to fully characterise the impact of regions on star formation.

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Section: Introductionmentioning

confidence: 99%

Stellar feedback in the star formation–gas density relation: Comparison between simulations and observations

Suin,

Zavagno,

Colman

et al. 2024

A&A

View full text Add to dashboard Cite

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“…The inferred current star-formation efficiencies range from about zero to a few percent in nearby clouds (e.g., Evans et al 2009;Forbrich et al 2009) to >10% for the most active clouds in the Milky Way (e.g., Galván-Madrid et al 2013;Louvet et al 2014;Ginsburg et al 2016). Several theoretical scenarios for MC evolution and star formation have been able to reproduce a variety observational restrictions (see, e.g., Federrath & Klessen 2012;Krumholz et al 2012;Vázquez-Semadeni et al 2019;Smith et al 2020;Hennebelle et al 2022). Among them, simulations of MCs that are under global collapse are particularly appealing for the regime of massive star formation in clustered environments (e.g., Vázquez-Semadeni et al 2007;Heitsch & Hartmann 2008;Colín et al 2013;Ibáñez-Mejía et al 2016;Zamora-Avilés et al 2019) because massive star-forming clumps appear to be dominated by self-gravity (e.g., Liu et al 2012;Lin et al 2016;Liu 2017).…”

Section: Introductionmentioning

confidence: 99%

Simulated Observations of Star Formation Regions: Infrared Evolution of Globally Collapsing Clouds

Jáquez-Domínguez

Galván-Madrid

Fritz

et al. 2023

ApJ

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A direct comparison between hydrodynamical simulations and observations is needed to improve the physics included in the former and to test biases in the latter. Post-processing radiative transfer and synthetic observations are now the standard way to do this. We report on the first application of the SKIRT radiative-transfer code to simulations of a star-forming cloud. The synthetic observations are then analyzed following traditional observational workflows. We find that in the early stages of the simulation, stellar radiation is inefficient in heating dust to the temperatures that are observed in Galactic clouds, thus the addition of an interstellar radiation field is necessary. The spectral energy distribution of the cloud settles rather quickly after ∼3 Myr of evolution from the onset of star formation, but its morphology continues to evolve for ∼8 Myr due to the expansion of H ii regions and the respective creation of cavities, filaments, and ridges. Modeling synthetic Herschel fluxes with one- or two-component modified blackbodies underestimates total dust masses by a factor of ∼2. However, spatially resolved fitting recovers up to about 70% of the intrinsic value. This “missing mass” is located in a very cold dust component with temperatures below 10 K, which does not contribute appreciably to the far-infrared flux. This effect could bias real observations if this dust exists in large amounts. Finally, we tested observational calibrations of the SFR based on infrared fluxes and concluded that they are in agreement when compared to the intrinsic SFR of the simulation averaged over ∼100 Myr.

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Alma-Imf

Pouteau¹,

Motte²,

T.³

et al. 2023

A&A

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Context. Among the most central open questions regarding the initial mass function (IMF) of stars is the impact of environment on the shape of the core mass function (CMF) and thus potentially on the IMF. Aims. The ALMA-IMF Large Program aims to investigate the variations in the core distributions (CMF and mass segregation) with cloud characteristics, such as the density and kinematic of the gas, as diagnostic observables of the formation process and evolution of clouds. The present study focuses on the W43-MM2&MM3 mini-starburst, whose CMF has recently been found to be top-heavy with respect to the Salpeter slope of the canonical IMF. Methods. W43-MM2&MM3 is a useful test case for environmental studies because it harbors a rich cluster that contains a statistically significant number of cores (specifically, 205 cores), which was previously characterized in Paper III. We applied a multi-scale decomposition technique to the ALMA 1.3 mm and 3 mm continuum images of W43-MM2&MM3 to define six subregions, each 0.5–1 pc in size. For each subregion we characterized the probability distribution function of the high column density gas, η-PDF, using the 1.3 mm images. Using the core catalog, we investigate correlations between the CMF and cloud and core properties, such as the η-PDF and the core mass segregation. Results. We classify the W43-MM2&MM3 subregions into different stages of evolution, from quiescent to burst to post-burst, based on the surface number density of cores, number of outflows, and ultra-compact HII presence. The high-mass end (>1 M⊙) of the subregion CMFs varies from close to the Salpeter slope (quiescent) to top-heavy (burst and post-burst). Moreover, the second tail of the η-PDF varies from steep (quiescent) to flat (burst and post-burst), as observed for high-mass star-forming clouds. We find that subregions with flat second η-PDF tails display top-heavy CMFs. Conclusions. In dynamical environments such as W43-MM2&MM3, the high-mass end of the CMF appears to be rooted in the cloud structure, which is at high column density and surrounds cores. This connection stems from the fact that cores and their immediate surroundings are both determined and shaped by the cloud formation process, the current evolutionary state of the cloud, and, more broadly, the star formation history. The CMF may evolve from Salpeter to top-heavy throughout the star formation process from the quiescent to the burst phase. This scenario raises the question of if the CMF might revert again to Salpeter as the cloud approaches the end of its star formation stage, a hypothesis that remains to be tested.

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Influence of magnetic field and stellar radiative feedback on the collapse and the stellar mass spectrum of a massive star-forming clump

Cited by 11 publications

References 102 publications

Stellar feedback in the star formation–gas density relation: Comparison between simulations and observations

Stellar feedback in the star formation–gas density relation: Comparison between simulations and observations

Simulated Observations of Star Formation Regions: Infrared Evolution of Globally Collapsing Clouds

Alma-Imf

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