Volume 575, March 2015
|Number of page(s)||17|
|Section||Interstellar and circumstellar matter|
|Published online||26 February 2015|
Understanding star formation in molecular clouds
Univ. Bordeaux, LAB, UMR 5804,
2 CNRS, LAB, UMR 5804, 33270 Floirac, France
3 I. Physikalisches Institut, Universität zu Köln, Zülpicher Straße 77, 50937 Köln, Germany
4 Max-Planck Institut für Radioastronomie, Auf dem Hügel 69, Bonn, Germany
5 Universität Heidelberg, Zentrum für Astronomie, Institut für Theoretische Astrophysik, 69120 Heidelberg, Germany
6 Kavli Institute for Particle Astrophysics and Cosmology, Stanford University, SLAC National Accelerator Laboratory, CA 94025, USA
7 Department of Astronomy and Astrophysics, University of California, Santa Cruz, CA 95064, USA
8 Monash Centre for Astrophysics, School of Mathematical Sciences, Monash University, VIC 3800, Australia
9 Research School of Astronomy & Astrophysics, The Australian National University, Canberra, ACT 2611, Australia
10 Astrophysics Group, University of Exeter, EX4 4 QL Exeter, UK
11 Maison de la Simulation, CEA-CNRS-INRIA-UPS-UVSQ, USR 3441, CEA Saclay, France
12 Max-Planck Institut für Astrophysik, 85741 Garching, Germany
13 IRFU/SAp CEA/DSM, Laboratoire AIM CNRS – Université Paris Diderot, 91191 Gif-sur-Yvette, France
Received: 4 February 2014
Accepted: 16 December 2014
Column-density maps of molecular clouds are one of the most important observables in the context of molecular cloud- and star-formation (SF) studies. With the Herschel satellite it is now possible to precisely determine the column density from dust emission, which is the best tracer of the bulk of material in molecular clouds. However, line-of-sight (LOS) contamination from fore- or background clouds can lead to overestimating the dust emission of molecular clouds, in particular for distant clouds. This implies values that are too high for column density and mass, which can potentially lead to an incorrect physical interpretation of the column density probability distribution function (PDF). In this paper, we use observations and simulations to demonstrate how LOS contamination affects the PDF. We apply a first-order approximation (removing a constant level) to the molecular clouds of Auriga and Maddalena (low-mass star-forming), and Carina and NGC 3603 (both high-mass SF regions). In perfect agreement with the simulations, we find that the PDFs become broader, the peak shifts to lower column densities, and the power-law tail of the PDF for higher column densities flattens after correction. All corrected PDFs have a lognormal part for low column densities with a peak at Av ~ 2 mag, a deviation point (DP) from the lognormal at Av(DP) ~ 4−5 mag, and a power-law tail for higher column densities. Assuming an equivalent spherical density distribution ρ ∝ r− α, the slopes of the power-law tails correspond to αPDF = 1.8, 1.75, and 2.5 for Auriga, Carina, and NGC 3603. These numbers agree within the uncertainties with the values of α ≈ 1.5,1.8, and 2.5 determined from the slope γ (with α = 1−γ) obtained from the radial column density profiles (N ∝ rγ). While α ~ 1.5−2 is consistent with a structure dominated by collapse (local free-fall collapse of individual cores and clumps and global collapse), the higher value of α > 2 for NGC 3603 requires a physical process that leads to additional compression (e.g., expanding ionization fronts). From the small sample of our study, we find that clouds forming only low-mass stars and those also forming high-mass stars have slightly different values for their average column density (1.8 × 1021 cm-2 vs. 3.0 × 1021 cm-2), and they display differences in the overall column density structure. Massive clouds assemble more gas in smaller cloud volumes than low-mass SF ones. However, for both cloud types, the transition of the PDF from lognormal shape into power-law tail is found at the same column density (at Av ~ 4−5 mag). Low-mass and high-mass SF clouds then have the same low column density distribution, most likely dominated by supersonic turbulence. At higher column densities, collapse and external pressure can form the power-law tail. The relative importance of the twoprocesses can vary between clouds and thus lead to the observed differences in PDF and column density structure.
Key words: dust, extinction / ISM: clouds / submillimeter: ISM / methods: data analysis / ISM: general
Appendices are available in electronic form at http://www.aanda.org
Herschel maps as FITS files are only available at the CDS via anonymous ftp to cdsarc.u-strasbg.fr (188.8.131.52) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/575/A79
© ESO, 2015
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