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arXiv · 1101.3324

The Infrared Properties of Super Star Clusters: Predictions from Three-Dimensional Radiative Transfer Models

Abstract

With high-resolution infrared data becoming available that can probe the formation of high-mass stellar clusters for the first time, models that make testable predictions of these objects are necessary. We utilize a three-dimensional radiative transfer code, including a hierarchically clumped medium, to study the earliest stages of super star cluster evolution. We explore a range of parameter space in geometric sequences that mimic the evolution of an embedded super star cluster. The inclusion of a hierarchically clumped medium can make the envelope porous, in accordance with previous models and supporting observational evidence. The infrared luminosity inferred from observations can differ by a factor of two from the true value in the clumpiest envelopes depending on the viewing angle. The infrared spectral energy distribution also varies with viewing angle for clumpy envelopes, creating a range in possible observable infrared colors and magnitudes, silicate feature depths and dust continua. General observable features of cluster evolution differ between envelopes that are relatively opaque or transparent to mid-infrared photons. The [70]-[160] color can be used to determine star formation efficiency; the Spitzer IRAC/MIPS [8.0]-[24] color is able to constrain Rin and Rout values; and the IRAC [3.6]-[5.8] color is sensitive to the fraction of the dust distributed in clumps. Finally, in a comparison of these models to data of ultracompact HII regions, we find good agreement, suggesting that these models are physically relevant, and will provide useful diagnostic ability for datasets of resolved, embedded SSCs with the advent of high-resolution infrared telescopes like JWST.

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BibTeXRIS

David G. Whelan, Kelsey E. Johnson, Barbara A. Whitney, Remy Indebetouw, Kenneth Wood. 2011-01-17. The Infrared Properties of Super Star Clusters: Predictions from Three-Dimensional Radiative Transfer Models. https://doi.org/10.1088/0004-637x%2F729%2F2%2F111

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