The Bias of Molecular Clump Identification Programs: the Example of the Carina Molecular Clouds

Schneider, N.; Brooks, Kate

doi:10.1071/as04012

Cited by 42 publications

(50 citation statements)

References 59 publications

(90 reference statements)

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“…The Rosette cloud has a similar total cloud mass than the CNC that is only slightly lower, and also harbors a large population of OB stars. However (as pointed out by Schneider & Brooks 2004;Schneider et al 1998), the Rosette Molecular Cloud is exposed to a much (about 10 times) weaker UV field than what we find in the Carina Nebula. This order-of-magnitude difference in the level of the radiative feedback may explain the differences in the cloud structure of these two complexes.…”

Section: Relative Importance Of Massive Star Feedback and Primordial contrasting

confidence: 44%

“…For example, in the PDR in the Orion KL region, G 0 values up to 10 4 −10 5 have been determined (see references in Schneider & Brooks 2004). Such high local values are, however, only found in clouds located very close (less than 1 parsec) to individual high-mass stars, i.e., restricted to very small cloud surface areas.…”

Section: Strength Of the Radiative Feedbackmentioning

confidence: 99%

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Herschelfar-infrared observations of the Carina Nebula complex

Roccatagliata

Preibisch

Ratzka

et al. 2013

A&A

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Context. The star formation process in large clusters/associations can be strongly influenced by the feedback from high-mass stars. Whether the resulting net effect of the feedback is predominantly negative (cloud dispersal) or positive (triggering of star formation due to cloud compression) is still an open question. Aims. The Carina Nebula complex (CNC) represents one of the most massive star-forming regions in our Galaxy. We use our Herschel far-infrared observations to study the properties of the clouds over the entire area of the CNC (with a diameter of ≈3.2 • , which corresponds to ≈125 pc at a distance of 2.3 kpc). The good angular resolution (10 −36 ) of the Herschel maps corresponds to physical scales of 0.1-0.4 pc, and allows us to analyze the small-scale (i.e., clump-size) structures of the clouds. Methods. The full extent of the CNC was mapped with PACS and SPIRE in the 70, 160, 250, 350, and 500 μm bands. We determined temperatures and column densities at each point in these maps by modeling the observed far-infrared spectral energy distributions. We also derived a map showing the strength of the UV radiation field. We investigated the relation between the cloud properties and the spatial distribution of the high-mass stars and computed total cloud masses for different density thresholds. Results. Our Herschel maps resolve for the first time the small-scale structure of the dense clouds over the entire spatial extent of the CNC. Several particularly interesting regions, including the prominent pillars south of η Car, are analyzed in detail. We compare the cloud masses derived from the Herschel data with previous mass estimates based on sub-mm and molecular line data. Our maps also reveal a peculiar wave-like pattern in the northern part of the Carina Nebula. Finally, we characterize two prominent cloud complexes at the periphery of our Herschel maps, which are probably molecular clouds in the Galactic background. Conclusions. We find that the density and temperature structure of the clouds in most parts of the CNC is dominated by the strong feedback from the numerous massive stars, and not by random turbulence. Comparing the cloud mass and the star formation rate derived for the CNC with other Galactic star-forming regions suggests that the CNC is forming stars in a particularly efficient way. We suggest this to be a consequence of triggered star formation by radiative cloud compression.

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Section: Relative Importance Of Massive Star Feedback and Primordial contrasting

confidence: 44%

Section: Strength Of the Radiative Feedbackmentioning

confidence: 99%

Herschelfar-infrared observations of the Carina Nebula complex

Roccatagliata

Preibisch

Ratzka

et al. 2013

A&A

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“…However, there is a long-lasting controversy about its validity in theory (e.g., Ballesteros-Paredes & Mac Low 2002) and observations (e.g., Schneider & Brooks 2004;Heyer et al 2009). Recently, a study of Lombardi et al (2010), using extinction maps of five molecular clouds, has concluded that all clouds in their sample have identical average column densities above a given extinction threshold.…”

Section: Discussionmentioning

confidence: 99%

Understanding star formation in molecular clouds

Schneider

Ossenkopf

Csengeri

et al. 2015

A&A

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Column-density maps of molecular clouds are one of the most important observables in the context of molecular cloud-and starformation (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 A v ∼ 2 mag, a deviation point (DP) from the lognormal at A v (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 α PDF = 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 ∝ r γ ). 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 × 10 21 cm −2 vs. 3.0 × 10 21 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 A v ∼ 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 two processes can vary between clouds and thus lead to the observed differences in PDF and column density structure.

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“…This could explain, for example, why many molecular line studies failed to detect a linewidth-size relation in molecular clumps (e.g. Loren 1989;Simon et al 2001;Schneider & Brooks 2004). In any case, it is difficult to transform model results into "observables" such as the 3D-or projected (on the plane of the sky) emission and velocity structure of an observed molecular line.…”

Section: Introductionmentioning

confidence: 99%

The link between molecular cloud structure and turbulence

Schneider

Bontemps

Simon

et al. 2011

A&A

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121

139

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Aims. We aim to better understand how the spatial structure of molecular clouds is governed by turbulence. For that, we study the large-scale spatial distribution of low-density molecular gas and search for characteristic length scales. Methods. We employ a 35 square degrees 13 CO 1→0 molecular line survey of Cygnus X and visual extinction (A V ) maps of 17 Galactic clouds to analyze the spatial structure with the Δ-variance method. This sample contains a large variety of different molecular cloud types with different star-forming activity.Results. The Δ-variance spectra obtained from the A V maps show differences between low-mass star-forming (SF) clouds and massive giant molecular clouds (GMC) in terms of shape of the spectrum and its power-law exponent β. Low-mass SF clouds have a double-peak structure with characteristic size scales around 1 pc (though with a large scatter around this value) and 4 pc. The GMCs show no characteristic scale in the A V -maps, which can partly be ascribed to a distance effect owing to a larger line-of-sight (LOS) confusion. The Δ-variance for Cygnus, determined from the 13 CO survey, shows characteristic scales at 4 pc and 40 pc, either reflecting the filament structure and large-scale turbulence forcing or -for the 4 pc scale -the scale below which the 13 CO 1→0 line becomes optically thick. Though there are different processes that can introduce characteristic scales, such as geometry, decaying turbulence, the transition scale from supersonic to subsonic turbulence (the sonic scale), line-of-sight effects and energy injection caused by expanding supernova shells, outflows, HII-regions, and although the relative contribution of these effects strongly varies from cloud to cloud, it is remarkable that the resulting turbulent structure of molecular clouds shows similar characteristics.

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The Bias of Molecular Clump Identification Programs: the Example of the Carina Molecular Clouds

Cited by 42 publications

References 59 publications

Herschelfar-infrared observations of the Carina Nebula complex

Herschelfar-infrared observations of the Carina Nebula complex

Understanding star formation in molecular clouds

The link between molecular cloud structure and turbulence

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