Star formation in cloud cores - simulations and observations of dense molecular cores and the formation of solar mass stars

Federrath, C., 2020, IAU Symposium 345, 43

Monthly Notices of the Royal Astronomical Society, 486, 3647  [ Publication link ]  [ PDF ]

Abstract

Star formation is inefficient. Recent advances in numerical simulations and theoretical models of molecular clouds show that the combined effects of interstellar turbulence, magnetic fields and stellar feedback can explain the low efficiency of star formation. The star formation rate is highly sensitive to the driving mode of the turbulence. Solenoidal driving may be more important in the Central Molecular Zone, compared to more compressive driving agents in spiral-am clouds. Both theoretical and observational efforts are underway to determine the dominant driving mode of turbulence in different Galactic environments. New observations with ALMA, combined with other instruments such as CARMA, JCMT and the SMA begin to reveal the magnetic field structure of dense cores and protostellar disks, showing highly complex field geometries with ordered and turbulent field components. Such complex magnetic fields can give rise to a range of stellar masses and jet/outflow efficiencies in dense cores and protostellar accretion disks.


Zoom-in on cosmic star formation.

Schematic zoom-in on cosmic star formation. The spatial range covered by star formation is more than a billion: from the cosmic web (size scales of order 10 Mega parsec) to galaxies, interstellar clouds, and nally to the protostellar disks (scales of order 1000 astronomical units) that spawn new stars and planets. Observing, modeling, and understanding the complex interplay of the physical and chemical processes across this huge range of scales from the cosmic web down to stars and planets is one of the biggest challenges in astrophysics. Images adopted from Taylor & Kobayashi (2015), Sharda et al. (2018), Arzoumanian et al. (2018), and Kuruwita et al. (2017).

Acknowledgements

C. F. acknowledges funding by the Australian Research Council (Discovery Projects DP170100603 and Future Fellowship FT180100495), and the Australia-Germany Joint Research Cooperation Scheme (UA-DAAD). We further acknowledge high-performance computing resources provided by the Leibniz Rechenzentrum and the Gauss Centre for Supercomputing (grants pr32lo, pr48pi and GCS Large-scale project 10391), the Partnership for Advanced Computing in Europe (PRACE grant pr89mu), the Australian National Computational Infrastructure (grant ek9), and the Pawsey Supercomputing Centre with funding from the Australian Government and the Government of Western Australia, in the framework of the National Computational Merit Allocation Scheme and the ANU Allocation Scheme. The simulation software FLASH was in part developed by the DOE-supported Flash Center for Computational Science at the University of Chicago.


© C. Federrath 2021