Issue |
A&A
Volume 600, April 2017
|
|
---|---|---|
Article Number | A11 | |
Number of page(s) | 16 | |
Section | Interstellar and circumstellar matter | |
DOI | https://doi.org/10.1051/0004-6361/201425486 | |
Published online | 20 March 2017 |
A modular set of synthetic spectral energy distributions for young stellar objects
1 Max Planck Institute for Astronomy, Königstuhl 17, 69117 Heidelberg, Germany
e-mail: thomas.robitaille@gmail.com
2 Headingley Enterprise and Arts Centre, Bennett Road, Leeds, LS6 3HN, UK
Received: 8 December 2014
Accepted: 3 August 2016
In this paper, I present a new set of synthetic spectral energy distributions (SEDs) for young stellar objects (YSOs) spanning a wide range of evolutionary stages, from the youngest deeply embedded protostars to pre-main-sequence stars with few or no disks. These models include significant improvements on the previous generation of published models: in particular, the new models cover a much wider and more uniform region of parameter space, do not include highly model-dependent parameters, and include a number of improvements that make them more suited to modeling far-infrared and sub-mm observations of forming stars. Rather than all being part of a single monolithic set of models, the new models are split up into sets of varying complexity. The aim of the new set of models is not to provide the most physically realistic models for young stars, but rather to provide deliberately simplified models for initial modeling, which allows a wide range of parameter space to be explored. I present the design of the model set, and show examples of fitting these models to real observations to show how the new grid design can help us better understand what can be determined from limited unresolved observations. The models, as well as a Python-based fitting tool are publicly available to the community.
Key words: astronomical databases: miscellaneous / radiative transfer / stars: formation / stars: protostars
© ESO, 2017
Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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