摘要: We present $\sim10-40\,\mu$m SOFIA-FORCAST images of 11 isolated protostars
as part of the SOFIA Massive (SOMA) Star Formation Survey, with this
morphological classification based on 37 $\mu$m imaging. We develop an
automated method to define source aperture size using the gradient of its
background-subtracted enclosed flux and apply this to build spectral energy
distributions (SEDs). We fit the SEDs with radiative transfer models, developed
within the framework of turbulent core accretion (TCA) theory, to estimate key
protostellar properties. Here, we release the sedcreator python package that
carries out these methods. The SEDs are generally well fitted by the TCA
models, from which we infer initial core masses $M_c$ ranging from
$20-430\:M_\odot$, clump mass surface densities $\Sigma_{\rm
cl}\sim0.3-1.7\:{\rm{g\:cm}}^{-2}$ and current protostellar masses
$m_*\sim3-50\:M_\odot$. From a uniform analysis of the 40 sources in the full
SOMA survey to date, we find that massive protostars form across a wide range
of clump mass surface density environments, placing constraints on theories
that predict a minimum threshold $\Sigma_{\rm cl}$ for massive star formation.
However, the upper end of the $m_*-\Sigma_{\rm cl}$ distribution follows trends
predicted by models of internal protostellar feedback that find greater star
formation efficiency in higher $\Sigma_{\rm cl}$ conditions. We also
investigate protostellar far-IR variability by comparison with IRAS data,
finding no significant variation over an $\sim$40 year baseline.
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分类:
天文学
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天文学
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引用:
ChinaXiv:202303.03108
(或此版本
ChinaXiv:202303.03108V1)
DOI:10.12074/202303.03108V1
CSTR:32003.36.ChinaXiv.202303.03108.V1
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科创链TXID:
3e39ddf8-dea2-4596-8576-256547676821
- 推荐引用方式:
Ruben Fedriani,Jonathan C. Tan,Zoie Telkamp,Yichen Zhang,Yao-Lun Yang,Mengyao Liu,Chi-Yan Law,Maria T. Beltran,Viviana Rosero,Kei E. I. Tanaka,Giuliana Cosentino,Prasanta Gorai,Juan Farias,Jan E. Staff,James M. De Buizer,Barbara Whitney.The SOFIA Massive (SOMA) Star Formation Survey. IV. Isolated Protostars.中国科学院科技论文预发布平台.[ChinaXiv:202303.03108V1]
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