On the indeterministic nature of star formation on the cloud scale

Abstract

Molecular clouds are turbulent structures whose star formation efficiency (SFE) is strongly affected by internal stellar feedback processes. In this paper, we determine how sensitive the SFE of molecular clouds is to randomized inputs in the star formation feedback loop, and to what extent relationships between emergent cloud properties and the SFE can be recovered. We introduce the YULE suite of 26 radiative magnetohydrodynamic simulations of a 10 000 solar mass cloud similar to those in the solar neighbourhood. We use the same initial global properties in every simulation but vary the initial mass function sampling and initial cloud velocity structure. The final SFE lies between 6 and 23 per cent when either of these parameters are changed. We use Bayesian mixed-effects models to uncover trends in the SFE. The number of photons emitted early in the cluster's life and the length of the cloud provide the strongest predictors of the SFE. The H II regions evolve following an analytic model of expansion into a roughly isothermal density field. The more efficient feedback is at evaporating the cloud, the less the star cluster is dispersed. We argue that this is because if the gas is evaporated slowly, the stars are dragged outwards towards surviving gas clumps due to the gravitational attraction between the stars and gas. While star formation and feedback efficiencies are dependent on non-linear processes, statistical models describing cloud-scale processes can be constructed.

Massive star feedback in clusters: variation of the FUV interstellar radiation field in time and space

“…If this makes the environment more permeable to ionizing photons, the total ionized mass could be increased. This bears more investigation as the method for forming stars differs between the two studies, as does the gas distribution when stars begin to radiate, and these differences can also change the effectiveness of feedback (Geen et al 2018). Dale et al (2013) also find a strong dependence on dispersal efficiency with cloud escape velocity.…”

“…Parameter studies by Dale & Bonnell (2011) and Dale et al (2012Dale et al ( , 2013 found that the degree of dispersal was closely coupled to the initial conditions of their simulations, such as cloud mass (see also Howard et al 2017). Dispersal has also been shown to depend on initial gas surface density (Kim et al 2018), morphology (Geen et al 2018), and cluster luminosity (Geen et al 2016(Geen et al , 2018. One problem is that even the most recent models simplify the radiative transfer, for example by using the on-the-spot approximation for recombination (thus neglecting the ionizing photons re-emitted by the gas), by using simple two-step temperature schemes for neutral and ionized hydrogen, or by neglecting dust microphysics such as absorption and scattering.…”

There is no magnetic braking catastrophe: low-mass star cluster and protostellar disc formation with non-ideal magnetohydrodynamics

“…There have been numerous numerical studies of the formation and evolution of large (M ≥ 10 3 M ) molecular clouds, that both exclude (e.g. Walch et al 2012;Dale et al 2014 Myers et al 2014;Geen et al 2015;Girichidis et al 2016;Geen et al 2018;Lee & Hennebelle 2019) magnetic fields. These studies allow for a direct comparison with observations on global (whole cloud) scales.…”

Shaping the structure of a GMC with radiation and winds

“…In their simulations SFE ∼ 0.3-0.6, suggesting that radiation feedback alone is not enough to suppress the SFE to the generally observed values (∼ 1-10 per cent; Lada, Lombardi & Alves 2010;Murray, Quataert & Thompson 2010;Lada 2016;Lee et al 2016;Ochsendorf et al 2017). However, other models show that a lower SFEs can be obtained by including magnetic fields in the simulation (Geen et al 2016(Geen et al , 2018Kim, Kim & Ostriker 2018;Haid et al 2019;He, Ricotti & Geen 2019). In our paper, we make a further step in the accuracy of GMC simulations, including several novel features:…”