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Published September 21, 2018 | Submitted
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The Elephant in the Room: The Importance of Where and When Massive Stars Form in Molecular Clouds

Abstract

Most simulations of galaxies and massive giant molecular clouds (GMCs) cannot explicitly resolve the formation (or predict the main-sequence masses) of individual stars. So they must use some prescription for the amount of feedback from an assumed population of massive stars (e.g. sampling the initial mass function [IMF]). We perform a methods study of simulations of a star-forming GMC with stellar feedback from UV radiation, varying only the prescription for determining the luminosity of each stellar mass element formed (according to different IMF sampling schemes). We show that different prescriptions can lead to widely varying (factor of ~3) star formation efficiencies (on GMC scales) even though the average mass-to-light ratios agree. Discreteness of sources is important: radiative feedback from fewer, more-luminous sources has a greater effect for a given total luminosity. These differences can dominate over other, more widely-recognized differences between similar literature GMC-scale studies (e.g. numerical methods, cloud initial conditions, presence of magnetic fields). Moreover the differences in these methods are not purely numerical: some make different implicit assumptions about where and how massive stars form, and this remains deeply uncertain in star formation theory.

Additional Information

We thank Mark Krumholz, Stella Offner, Claude-André Faucher-Giguère, Norman Murray, Eliot Quataert, James Dale, Ian Bonnell, Hui Li, Jeong-Gyu Kim, Eve Ostriker, Michael S. Fall, Christopher Matzner, Benny Tsang, and Milos Milosavljević for enlightening discussions that informed and motivated this work. Support for MYG and PFH was provided by a James A Cullen Memorial Fellowship, an Alfred P. Sloan Research Fellowship, NSF Collaborative Research Grant #1715847 and CAREER grant #1455342, and NASA grants NNX15AT06G, JPL 1589742, 17-ATP17-0214. Numerical calculations were run on the Caltech compute cluster "Wheeler," allocations from XSEDE TG-AST130039 and PRAC NSF.1713353 supported by the NSF, and NASA HEC SMD-16-7592. This research has made use of the NASA Astrophysics Data System, ipython (Pérez & Granger 2007), numpy, scipy (Jones et al. 2001), numba (Lam et al. 2015), and matplotlib (Hunter 2007).

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