ORCID Profile
0000-0002-3758-552X
Current Organisation
Australian National University
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Publisher: Oxford University Press (OUP)
Date: 28-03-2023
Abstract: Mass-to-flux ratios measured via the Zeeman effect suggest the existence of a transition from a magnetically sub-critical state in H i clouds to a supercritical state in molecular clouds. However, due to projection, chemical, and excitation effects, Zeeman measurements are subject to a number of biases, and may not reflect the true relations between gravitational and magnetic energies. In this paper, we carry out simulations of the formation of magnetized molecular clouds, zooming in from an entire galaxy to sub-pc scales, which we post-process to produce synthetic H i and OH Zeeman measurements. The mass-to-flux ratios we recover from the simulated observations show a transition in magnetic criticality that closely matches observations, but we find that the gravitational-magnetic energy ratios on corresponding scales are mostly supercritical, even in the H i regime. We conclude that H i clouds in the process of assembling to form molecular clouds are already supercritical even before H2 forms, and that the apparent transition from sub- to supercriticality between H i and H2 is primarily an illusion created by chemical and excitation biases affecting the Zeeman measurements.
Publisher: Oxford University Press (OUP)
Date: 29-01-2022
Abstract: On average molecular clouds convert only a small fraction εff of their mass into stars per free-fall time, but different star formation theories make contrasting claims for how this low mean efficiency is achieved. To test these theories, we need precise measurements of both the mean value and the scatter of εff, but high-precision measurements have been difficult because they require determining cloud volume densities, from which we can calculate free-fall times. Until recently, most density estimates treated clouds as uniform spheres, while their real structures are often filamentary and highly non-uniform, yielding systematic errors in εff estimates and smearing real cloud-to-cloud variations. We recently developed a theoretical model to reduce this error by using column density distributions in clouds to produce more accurate volume density estimates. In this work, we apply this model to recent observations of 12 nearby molecular clouds. Compared to earlier analyses, our method reduces the typical dispersion of εff within in idual clouds from 0.16 dex to 0.12 dex, and decreases the median value of εff over all clouds from ≈0.02 to ≈0.01. However, we find no significant change in the ≈0.2 dex cloud-to-cloud dispersion of εff, suggesting the measured dispersions reflect real structural differences between clouds.
Publisher: Oxford University Press (OUP)
Date: 09-02-2021
Abstract: Star formation has long been known to be an inefficient process, in the sense that only a small fraction ϵff of the mass of any given gas cloud is converted to stars per cloud free-fall time. However, developing a successful theory of star formation will require measurements of both the mean value of ϵff and its scatter from one molecular cloud to another. Because ϵff is measured relative to the free-fall time, such measurements require accurate determinations of cloud volume densities. Efforts to measure the volume density from two-dimensional projected data, however, have thus far relied on treating molecular clouds as simple uniform spheres, while their real shapes are likely to be filamentary and their density distributions far from uniform. The resulting uncertainty in the true volume density is likely to be one of the major sources of error in observational estimates of ϵff. In this paper, we use a suite of simulations of turbulent, magnetized, radiative, self-gravitating star-forming clouds in order to examine whether it is possible to obtain more accurate volume density estimates and thereby reduce this error. We create mock observations from the simulations, and show that current analysis methods relying on the spherical assumption likely yield ∼0.26 dex underestimations and ∼0.51 dex errors in volume density estimates, corresponding to a ∼0.13 dex overestimation and a ∼0.25 dex scatter in ϵff, comparable to the scatter in observed cloud s les. We build a predictive model that uses information accessible in two-dimensional measurements – most significantly, the Gini coefficient of the surface density distribution – to produce estimates of the volume density with ∼0.3 dex less scatter. We test our method on a recent observation of the Ophiuchus cloud, and show that it successfully reduces the ϵff scatter.
No related grants have been discovered for Zipeng HU.