2022MNRAS.511.1431H

On average molecular clouds convert only a small fraction ε_ff 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 ε_ff, 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 ε_ff 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 ε_ff within individual clouds from 0.16 to 0.12 dex, and decreases the median value of ε_ff 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 ε_ff, suggesting the measured dispersions reflect real structural differences between clouds.