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arXiv · 0705.4196

The AU Microscopii Debris Disk: Multiwavelength Imaging and Modeling

Abstract

(abridged) Debris disks around main sequence stars are produced by the erosion and evaporation of unseen parent bodies. AU Microscopii (GJ 803) is a compelling object to study in the context of disk evolution across different spectral types, as it is an M dwarf whose near edge-on disk may be directly compared to that of its A5V sibling beta Pic. We resolve the disk from 8-60 AU in the near-IR JHK' bands at high resolution with the Keck II telescope and adaptive optics, and develop a novel data reduction technique for the removal of the stellar point spread function. The point source detection sensitivity in the disk midplane is more than a magnitude less sensitive than regions away from the disk for some radii. We measure a blue color across the near-IR bands, and confirm the presence of substructure in the inner disk. Some of the structural features exhibit wavelength-dependent positions. The disk architecture and characteristics of grain composition are inferred through modeling. We approach the modeling of the dust distribution in a manner that complements previous work. Using a Monte Carlo radiative transfer code, we compare a relatively simple model of the distribution of porous grains to a broad data set, simultaneously fitting to midplane surface brightness profiles and the spectral energy distribution. Our model confirms that the large-scale architecture of the disk is consistent with detailed models of steady-state grain dynamics. Here, a belt of parent bodies from 35-40 AU is responsible for producing dust that is then swept outward by the stellar wind and radiation pressures. We infer the presence of very small grains in the outer region, down to sizes of ~0.05 micron. These sizes are consistent with stellar mass-loss rates Mdot_* << 10^2 Mdot_sun.

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BibTeXRIS

Michael P. Fitzgerald, Paul G. Kalas, Gaspard Duchene, Christophe Pinte, James R. Graham. 2007-05-29. The AU Microscopii Debris Disk: Multiwavelength Imaging and Modeling. https://doi.org/10.1086/521344

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