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Open AccessJournal ArticleDOI

Efficiently Cooled Stellar Wind Bubbles in Turbulent Clouds. I. Fractal Theory and Application to Star-forming Clouds

TLDR
In this article, the authors developed a theory for the evolution of bubbles driven by the collective winds from star clusters early in their lifetimes, which involves interaction with the turbulent, dense interstellar medium of the surrounding natal molecular cloud.
Abstract
Winds from massive stars have velocities of 1000 km/s or more, and produce hot, high pressure gas when they shock. We develop a theory for the evolution of bubbles driven by the collective winds from star clusters early in their lifetimes, which involves interaction with the turbulent, dense interstellar medium of the surrounding natal molecular cloud. A key feature is the fractal nature of the hot bubble's surface. The large area of this interface with surrounding denser gas strongly enhances energy losses from the hot interior, enabled by turbulent mixing and subsequent cooling at temperatures T = 10^4-10^5 K where radiation is maximally efficient. Due to the extreme cooling, the bubble radius scales differently (R ~ t^1/2) from the classical Weaver77 solution, and has expansion velocity and momentum lower by factors of 10-10^2 at given R, with pressure lower by factors of 10^2 - 10^3. Our theory explains the weak X-ray emission and low shell expansion velocities of observed sources. We discuss further implications of our theory for observations of the hot bubbles and cooled expanding shells created by stellar winds, and for predictions of feedback-regulated star formation in a range of environments. In a companion paper, we validate our theory with a suite of hydrodynamic simulations.

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Osaka Feedback Model. II. Modeling Supernova Feedback Based on High-resolution Simulations

TL;DR: In this article , an Eulerian hydrodynamic code Athena++ was used to find universal scaling relations for the time evolution of momentum and radius for a superbubble, when the momentum and time are scaled by those at the shell-formation time.
Journal ArticleDOI

Far and extreme UV radiation feedback in molecular clouds and its influence on the mass and size of star clusters

TL;DR: In this article , the formation of star clusters in molecular clouds was studied by performing three-dimensional radiation hydrodynamics simulations with far ultraviolet (FUV; 6 eV≦hν≦13.6 eV) radiative feedback.
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Infrared Radiation Feedback Does Not Regulate Star Cluster Formation

TL;DR: In this article , a 3D radiation-hydrodynamical (RHD) simulation of star cluster formation and evolution in massive, self-gravitating clouds, whose dust columns are optically thick to infrared (IR) photons is presented.
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Filamentary structures of ionized gas in Cygnus X

TL;DR: In this paper , the authors used DisPerSE and FilChaP to identify filamentary structures and characterize their radial (EM) pro�les in the Cygnus X region.
References
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Journal ArticleDOI

Matplotlib: A 2D Graphics Environment

TL;DR: Matplotlib is a 2D graphics package used for Python for application development, interactive scripting, and publication-quality image generation across user interfaces and operating systems.
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On the variation of the initial mass function

TL;DR: In this paper, the uncertainty inherent in any observational estimate of the IMF is investigated by studying the scatter introduced by Poisson noise and the dynamical evolution of star clusters, and it is found that this apparent scatter reproduces quite well the observed scatter in power-law index determinations, thus defining the fundamental limit within which any true variation becomes undetectable.
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Star formation in galaxies along the hubble sequence

TL;DR: In this article, the authors focus on the broad patterns in the star formation properties of galaxies along the Hubble sequence and their implications for understanding galaxy evolution and the physical processes that drive the evolution.
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SciPy 1.0: fundamental algorithms for scientific computing in Python.

TL;DR: SciPy as discussed by the authors is an open-source scientific computing library for the Python programming language, which has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year.
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