The sonic scale of interstellar turbulence

Federrath, Christoph; Klessen, Ralf S.; Iapichino, Luigi; Beattie, James A.

doi:10.1038/s41550-020-01282-z

Cited by 123 publications

(106 citation statements)

References 60 publications

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“…Various semi-analytical models of turbulence-regulated star formation suggest that the collapse occurs approximately at the sonic scale (Federrath et al 2021), where the turbulent velocity dispersion is of the order of the thermal sound speed (Krumholz & McKee 2005;. Below the sonic scale, gravity dominates over any other form of energy (e.g., turbulence, magnetic fields, thermal pressure) that would oppose the collapse.…”

Section: Turbulence-regulated Star Formationmentioning

confidence: 99%

Impact of relativistic jets on the star formation rate: a turbulence-regulated framework

Mandal,

Mukherjee,

Federrath

et al. 2021

Preprint

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We apply a turbulence-regulated model of star formation to calculate the star formation rate (SFR) of dense star-forming clouds in simulations of jet-ISM interactions. The method isolates individual clumps and accounts for the impact of virial parameter and Mach number of the clumps on the star formation activity. This improves upon other estimates of the SFR in simulations of jet-ISM interactions, which are often solely based on local gas density, neglecting the impact of turbulence. We apply this framework to the results of a suite of jet-ISM interaction simulations to study how the jet regulates the SFR both globally and on the scale of individual star-forming clouds. We find that the jet strongly affects the multi-phase ISM in the galaxy, inducing turbulence and increasing the velocity dispersion within the clouds. This causes a global reduction in the SFR compared to a simulation without a jet. The shocks driven into clouds by the jet also compress the gas to higher densities, resulting in local enhancements of the SFR. However, the velocity dispersion in such clouds is also comparably high, which results in a lower SFR than would be observed in galaxies with similar gas mass surface densities and without powerful radio jets. We thus show that both local negative and positive jet feedback can occur in a single system during a single jet event, and that the star-formation rate in the ISM varies in a complicated manner that depends on the strength of the jet-ISM coupling and the jet break-out time-scale.

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Section: Turbulence-regulated Star Formationmentioning

confidence: 99%

Impact of relativistic jets on the star formation rate: a turbulence-regulated framework

Mandal,

Mukherjee,

Federrath

et al. 2021

Preprint

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“…Recently, Arzoumanian et al (2019) extended the analysis of filament widths to a much larger sample of filaments in the Herschel Gould Belt Survey (HGBS), finding results in agreement with their earlier study. Multiple theoretical models have been proposed to explain the observed distribution of widths and the apparent independence with the column density (Fischera & Martin 2012a,b;Hennebelle 2013;Hennebelle & André 2013;Federrath 2016;Auddy et al 2016;Federrath et al 2021;. To date, no model has been able to reproduce the properties of the distribution over the wide range of filament column densities in the sample of Arzoumanian et al (2019).…”

Section: Introductionmentioning

confidence: 99%

The width of Herschel filaments varies with distance

Panopoulou

Clark

Hacar

et al. 2022

A&A

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Context. Filamentary structures in nearby molecular clouds have been found to exhibit a characteristic width of 0.1 pc, as observed in dust emission. Understanding the origin of this universal width has become a topic of central importance in the study of molecular cloud structure and the early stages of star formation. Aims. We investigate how the recovered widths of filaments depend on the distance from the observer by using previously published results from the Herschel Gould Belt Survey. Methods. We obtained updated estimates on the distances to nearby molecular clouds observed with Herschel by using recent results based on 3D dust extinction mapping and Gaia. We examined the widths of filaments from individual clouds separately, as opposed to treating them as a single population. We used these per-cloud filament widths to search for signs of variation amongst the clouds of the previously published study. Results. We find a significant dependence of the mean per-cloud filament width with distance. The distribution of mean filament widths for nearby clouds is incompatible with that of farther away clouds. The mean per-cloud widths scale with distance approximately as 4−5 times the beam size. We examine the effects of resolution by performing a convergence study of a filament profile in the Herschel image of the Taurus Molecular Cloud. We find that resolution can severely affect the shapes of radial profiles over the observed range of distances. Conclusions. We conclude that the data are inconsistent with 0.1 pc being the universal characteristic width of filaments.

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“…Turbulence is included by constructing a Gaussian random vector field for the three velocity components, following the Fourier method described in Federrath et al (2010a). The power spectrum of the initial velocity fluctuations follows a 𝑃 𝑣 (𝑘) ∝ 𝑘 −2 spectrum, in the wavenumber range 2 ≤ 𝑘/(2𝜋/𝐿) ≤ 20, consistent with the observed velocity scaling in molecular clouds (Larson 1981;Solomon et al 1987;Ossenkopf & Mac Low 2002;Heyer & Brunt 2004;Roman-Duval et al 2011) and the statistics of supersonic turbulence (Kritsuk et al 2007;Federrath 2013;Federrath et al 2021). Here we set the velocity dispersion on scale 𝐿/2 (i.e., for the entire, cubical computational domain) to 1 km s −1 .…”

Section: Numerical Simulationsmentioning

confidence: 66%

A new method for measuring the 3D turbulent velocity dispersion of molecular clouds

Stewart,

Federrath

2021

Preprint

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The structure and star formation activity of a molecular cloud are fundamentally linked to its internal turbulence. However, accurately measuring the turbulent velocity dispersion is challenging due to projection effects and observational limitations, such as telescope resolution, particularly for clouds that include non-turbulent motions, such as large-scale rotation. Here we develop a new method to recover the three-dimensional (3D) turbulent velocity dispersion (𝜎 𝑣,3D ) from position-positionvelocity (PPV) data. We simulate a rotating, turbulent, collapsing molecular cloud and compare its intrinsic 𝜎 𝑣,3D with three different measures of the velocity dispersion accessible in PPV space: 1) the spatial mean of the 2nd-moment map, 𝜎 i , 2) the standard deviation of the gradient/rotation-corrected 1st-moment map, 𝜎 (c−grad) , and 3) a combination of 1) and 2), called the 'gradient-corrected parent velocity dispersion', 𝜎 (p−grad) = (𝜎 2 i + 𝜎 2 (c−grad) ) 1/2 . We show that the gradient correction is crucial in order to recover purely turbulent motions of the cloud, independent of the orientation of the cloud with respect to the line of sight (LOS). We find that with a suitable correction factor and appropriate filters applied to the moment maps, all three statistics can be used to recover 𝜎 𝑣,3D , with method 3 being the most robust and reliable. We determine the correction factor as a function of the telescope beam size for different levels of cloud rotation, and find that for a beam FWHM 𝑓 and cloud radius 𝑅, the 3D turbulent velocity dispersion can best be recovered from the gradient-corrected parent velocity dispersion via 𝜎 𝑣 ,3D = [(−0.29 ± 0.26) 𝑓 /𝑅 + 1.93 ± 0.15] 𝜎 (p−grad) for 𝑓 /𝑅 < 1, independent of the level of cloud rotation or LOS orientation.

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The sonic scale of interstellar turbulence

Cited by 123 publications

References 60 publications

Impact of relativistic jets on the star formation rate: a turbulence-regulated framework

Impact of relativistic jets on the star formation rate: a turbulence-regulated framework

The width of Herschel filaments varies with distance

A new method for measuring the 3D turbulent velocity dispersion of molecular clouds

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