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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
physics and astronomy
350 μm map of the Ophiuchus molecular cloud: core mass function
Science China: Physics, Mechanics and Astronomy, Volume 58, No. 2, Year 2015
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Description
Stars are born in dense cores of molecular clouds. The core mass function (CMF), which is the mass distribution of dense cores, is important for understanding the stellar initial mass function (IMF). We obtained 350 μm dust continuum data using the SHARC-II camera at the Caltech Submillimeter Observatory (CSO) telescope. A 350 μm map covering 0.25 deg2 of the Ophiuchus molecular cloud was created by mosaicing 56 separate scans. The CSO telescope had an angular resolution of 9″, corresponding to 1.2 × 103 AU at the distance of the Ophiuchus molecular cloud (131 pc). The data was reduced using the Comprehensive Reduction Utility for SHARC-II (CRUSH). The flux density map was analyzed using the GaussClumps algorithm, within which 75 cores has been identified. We used the Spitzer c2d catalogs to separate the cores into 63 starless cores and 12 protostellar cores. By locating Jeans instabilities, 55 prestellar cores (a subcategory of starless cores) were also identified. The excitation temperatures, which were derived from FCRAO 12CO data, help to improve the accuracy of the masses of the cores. We adopted a Monte Carlo approach to analyze the CMF with two types of functional forms; power law and log-normal. The whole and prestellar CMF are both well fitted by a log-normal distribution, with µ = −1.18 ± 0.10, σ = 0.58 ± 0.05 and µ = 1.40 ± 0.10, σ = 0.50 ± 0.05 respectively. This finding suggests that turbulence influences the evolution of the Ophiuchus molecular cloud. © 2014, Science China Press and Springer-Verlag Berlin Heidelberg.
Authors & Co-Authors
Zhang, Guoyin
China, Guiyang
Guizhou University
China, Beijing
Chinese Academy of Sciences
Li, D. H.
China, Beijing
Chinese Academy of Sciences
United States, Boulder
Space Science Institute
United States, Pasadena
California Institute of Technology
Hyde, Ashley K.
United Kingdom, London
Imperial College London
Qian, Lei
China, Beijing
Chinese Academy of Sciences
Statistics
Citations: 1
Authors: 4
Affiliations: 5
Identifiers
Doi:
10.1007/s11433-014-5561-7
ISSN:
16747348