Bias‐free Measurement of Giant Molecular Cloud Properties

By Erik Rosolowsky, A. Leroy. 2006

237 citations recorded by the source corpus.

We review methods for measuring the sizes, line widths, and luminosities of giant molecular clouds (GMCs) in molecular‐line data cubes with low resolution and sensitivity. We find that moment methods are robust and sensitive, making full use of both position and intensity information, and we recommend a standard method to measure the position angle, major and minor axis sizes, line width, and luminosity using moment methods. Without corrections for the effects of beam convolution and sensitivity to GMC properties, the resulting properties may be severely biased. This is particularly true for extragalactic observations, where resolution and sensitivity effects often bias measured values by 40% or more. We correct for finite spatial and spectral resolutions with a simple deconvolution, and we correct for sensitivity biases by extrapolating properties of a GMC to those we would expect to measure with perfect sensitivity (i.e., the 0 K isosurface). The resulting method recovers the properties of a GMC to within 10% over a large range of resolutions and sensitivities, provided the clouds are marginally resolved with a peak signal‐to‐noise ratio greater than 10. We note that interferometers systematically underestimate cloud properties, particularly the flux from a cloud. The degree of bias depends on the sensitivity of the observations and the (u,v) coverage of the observations. In an Appendix to the paper we present a conservative, new decomposition algorithm for identifying GMCs in molecular‐line observations. This algorithm treats the data in physical rather than observational units (i.e., parsecs rather than beams or arcseconds), does not produce spurious clouds in the presence of noise, and is sensitive to a range of morphologies. As a result, the output of this decomposition should be directly comparable among disparate data sets.

Subfields: Astronomy and Astrophysics, Instrumentation

View Paper: https://www.semanticscholar.org/paper/b3124a0785312b3289fc95c857bde1e37960015a

How researchers see this paper

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In Astronomy and Astrophysics

1. Mark R. Krumholz
2. A. Leroy
3. P. Hopkins
4. F. Walter
5. E. Ostriker
6. N. Murray
7. L. Hernquist
8. A. Bolatto

In Instrumentation

1. A. Leroy
2. F. Walter
3. A. Bolatto
4. Erik Rosolowsky
5. J. Kruijssen
6. Mark R. Krumholz
7. T. Dame
8. S. Longmore

Citations in the corpus

• PHYSICAL PROPERTIES OF MOLECULAR CLOUDS FOR THE ENTIRE MILKY WAY DISK (2016, background): “…Canada Research Chair in Astrophysics 4 Astronomy Department, University of California, Berkeley, CA 94720, USA; evelee@berkeley.edu (1987, 1988), Bronfman et al. (1988), Rosolowsky & Leroy (2006), Nakanishi & Sofue (2006) and Rice et al. (2016) to infer global properties of the molecular medium.”
• Dense gas in the Galactic central molecular zone is warm and heated by turbulence (2015, methodology): “16 The effective radius is often computed as the square root of the total area (in pixels), but Rosolowsky & Leroy (2006) show that this is usually a very bad approximation, so we use the Gaussian-corrected (see next footnote) moment-derived radius.”
• The CO-to-H2 Conversion Factor (2013, methodology): “Different methods applied to the same Milky Way data shift results by ∼ 30%, and biases of ∼ 40% are common in extragalactic data (e.g., Rosolowsky & Leroy 2006).”
• THE SECOND SURVEY OF THE MOLECULAR CLOUDS IN THE LARGE MAGELLANIC CLOUD BY NANTEN. II. STAR FORMATION (2009, methodology): “Fukui et al. (2008) identified 272 molecular clouds of which 230 are detected at more than two observing positions (hereafter “GMCs,” in this paper) by using cloud identifying algorithm, cprops (Rosolowsky & Leroy 2006).”
• Cloud-scale Molecular Gas Properties in 15 Nearby Galaxies (2018, methodology): “This comparative approach has often been used in the extragalactic literature (e.g., Rosolowsky & Leroy 2006; Bolatto et al. 2008), and this is the view we adopt in this work.”
• THE PdBI ARCSECOND WHIRLPOOL SURVEY (PAWS): ENVIRONMENTAL DEPENDENCE OF GIANT MOLECULAR CLOUD PROPERTIES IN M51 (2014, methodology): “We used the CPROPS package (Rosolowsky & Leroy 2006, hereafter RL06) to identify GMCs and measure their physical properties.”
• Structural Analysis of Molecular Clouds: Dendrograms (2008, methodology): “This method is described in more detail in Rosolowsky & Leroy (2006), in particular in their Figure 2.”
• The Resolved Properties of Extragalactic Giant Molecular Clouds (2008, methodology): “Apportion Emission Into Clouds Once signal is identified, data is apportioned into individual clouds following Rosolowsky & Leroy (2006).”
• Molecular Clouds in the Milky Way (2015, methodology): “In cases in which the thresholds are high, the derived properties are extrapolated to a lower surface brightness threshold (typically, TB = 1 K) to account for material in the extended cloud envelopes (Scoville et al. 1987, Solomon et al. 1987, Rosolowsky & Leroy 2006).”
• The Second Survey of the Molecular Clouds in the Large Magellanic Cloud by NANTEN. I. Catalog of Molecular Clouds (2008, methodology): “We have applied the cloud identifying algorithm by Rosolowsky & Leroy (2006) with the parameters used in the current study to identify the GMCs in the LMC to the molecular clouds in the Small Magellanic Cloud (SMC, Mizuno et al. 2001a) as well as in the outer Galaxy, the Warp region (Nakagawa et…”
• The lifecycle of molecular clouds in nearby star-forming disc galaxies (2019, methodology): “For example, applying the cloud characterisation algorithm CPROPS (Rosolowsky & Leroy 2006) to the CO maps leads in some cases to the identification of GMC complexes of several 100 pc in size (larger than λ), rather than individual GMCs.”
• THE MAGELLANIC MOPRA ASSESSMENT (MAGMA). I. THE MOLECULAR CLOUD POPULATION OF THE LARGE MAGELLANIC CLOUD (2011, methodology): “We present three separate catalogs of CO emission structures identified using the automated CPROPS algorithm (Rosolowsky & Leroy 2006).”