The High-mass Protostellar Population of a Massive Infrared Dark Cloud
Journal article, 2020

We conduct a census of the high-mass protostellar population of the similar to 70,000Minfrared dark cloud (IRDC) G028.37+00.07, identifying 35 sources based on their 70 mu m emission, as reported in the Herschel Hi-GAL catalog of Molinari et al. We perform aperture photometry to construct spectral energy distributions, which are then fit with the massive protostar models of Zhang & Tan. We find that the sources span a range of isotropic luminosities from similar to 20 to 4500L. The most luminous sources are predicted to have current protostellar masses ofm(*) similar to 10Mforming from cores of massM(c) similar to 40 to 400M. The least luminous sources in our sample are predicted to be protostars with masses as low as similar to 0.5Mforming from cores withM(c) similar to 10M, which are the minimum values explored in the protostellar model grid. The detected protostellar population has a total estimated protostellar mass ofM(*) similar to 100M. Allowing for completeness corrections, which are constrained by comparison with an ALMA study in part of the cloud, we estimate a star formation efficiency per freefall time of similar to 3% in the IRDC. Finally, analyzing the spatial distribution of the sources, we find relatively low degrees of central concentration of the protostars. The protostars, including the most massive ones, do not appear to be especially centrally concentrated in the protocluster as defined by the IRDC boundary.

Star formation

Protostars

Infrared photometry

Author

Emily Moser

Cornell University

Mengyao Liu

University of Virginia

Jonathan Tan

University of Virginia

Chalmers, Space, Earth and Environment, Astronomy and Plasmaphysics, Galactic Astrophysics

Wanggi Lim

Sofia Science Center

Yichen Zhang

RIKEN

Juan Pablo Farias Osses

Chalmers, Space, Earth and Environment, Astronomy and Plasmaphysics, Galactic Astrophysics

Astrophysical Journal

0004-637X (ISSN) 1538-4357 (eISSN)

Vol. 897 2 136

Subject Categories

Meteorology and Atmospheric Sciences

Astronomy, Astrophysics and Cosmology

Probability Theory and Statistics

DOI

10.3847/1538-4357/ab96c1

More information

Latest update

9/3/2020 9