Issue |
A&A
Volume 608, December 2017
|
|
---|---|---|
Article Number | A13 | |
Number of page(s) | 13 | |
Section | Galactic structure, stellar clusters and populations | |
DOI | https://doi.org/10.1051/0004-6361/201630193 | |
Published online | 29 November 2017 |
A new method to unveil embedded stellar clusters
1 University of Milan, Department of Physics, via Celoria 16, 20133 Milan, Italy
e-mail: marco.lombardi@unimi.it
2 Harvard-Smithsonian Center for Astrophysics, Mail Stop 72, 60 Garden Street, Cambridge, MA 02138, USA
3 University of Vienna, Türkenschanzstrasse 17, 1180 Vienna, Austria
Received: 5 December 2016
Accepted: 25 July 2017
In this paper we present a novel method to identify and characterize stellar clusters deeply embedded in a dark molecular cloud. The method is based on measuring stellar surface density in wide-field infrared images using star counting techniques. It takes advantage of the differing H-band luminosity functions (HLFs) of field stars and young stellar populations and is able to statistically associate each star in an image as a member of either the background stellar population or a young stellar population projected on or near the cloud. Moreover, the technique corrects for the effects of differential extinction toward each individual star. We have tested this method against simulations as well as observations. In particular, we have applied the method to 2MASS point sources observed in the Orion A and B complexes, and the results obtained compare very well with those obtained from deep Spitzer and Chandra observations where presence of infrared excess or X-ray emission directly determines membership status for every star. Additionally, our method also identifies unobscured clusters and a low resolution version of the Orion stellar surface density map shows clearly the relatively unobscured and diffuse OB 1a and 1b sub-groups and provides useful insights on their spatial distribution.
Key words: methods: statistical / ISM: clouds / open clusters and associations: general
© ESO, 2017
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