CHIMPS: physical properties of molecular clumps across the inner Galaxy

Rigby, Andrew; Moore, T. J. T.; Eden, David; Urquhart, J. S.; Ragan, S. E.; Peretto, N.; Plume, R.; Currie, Malcolm J.; Park, Gyueun

doi:10.1051/0004-6361/201935236

Cited by 50 publications

(108 citation statements)

References 81 publications

(124 reference statements)

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“…12). This is somewhat surprising, given that Rigby et al (2019) used the BDC without the SA prior for their distance calculation, which should have yielded a better agreement with our Run B. However, we note that Rigby et al (2019) used a higher-density tracer ( 13 CO (3-2)) and v1 of the BDC 14 , which could both account for any differences compared to our results.…”

Section: Comparison With Previous Resultscontrasting

confidence: 52%

“…CHIMPS clumps. Rigby et al (2019) used literature information to resolve the KDA for their sample of clumps compiled from the 13 CO/C 18 O (J = 3-2) Heterodyne Inner Milky Way Plane Survey (CHIMPS; Rigby et al 2016). We use 3294 clumps that have the highest reliability flag and a resolved KDA.…”

Section: A3 Literature Results Used In the Kda Priormentioning

confidence: 99%

“…Urquhart et al 2018), recent studies have found no significant impact of Galactic structure on the clump or star formation efficiency of dense clumps (e.g. Moore et al 2012;Eden et al 2013Eden et al , 2015Ragan et al 2016Ragan et al , 2018, or the physical properties of filaments (Schisano et al 2020) and molecular clumps (Rigby et al 2019). However, the last study reported differences in the linewidths between clumps located in interarm and spiral arm structures.…”

Section: Introductionmentioning

confidence: 94%

“…Many studies have focussed on extracting structures from these surveys, which have been compiled into catalogues of physical objects such as molecular clouds and clumps (e.g. Solomon et al 1987;Rathborne et al 2009;Rice et al 2016;Miville-Deschênes et al 2017;Rigby et al 2019). Alternative approaches (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…New distance estimation approaches have emerged that use a Bayesian approach to combine these parallax measurements with additional information from CO and H I surveys (Reid et al 2016(Reid et al , 2019, which has already been used in the distance estimation to molecular clouds and clumps (e.g. Rice et al 2016;Urquhart et al 2018;Rigby et al 2019;Duarte-Cabral, in prep. ).…”

Section: Introductionmentioning

confidence: 99%

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Autonomous Gaussian decomposition of the Galactic Ring Survey

Riener

Kainulainen

Henshaw

et al. 2020

A&A

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Knowledge about the distribution of CO emission in the Milky Way is essential to understanding the impact of the Galactic environment on the formation and evolution of structures in the interstellar medium. However, our current insight as to the fraction of CO in the spiral arm and interarm regions is still limited by large uncertainties in assumed rotation curve models or distance determination techniques. In this work we use the Bayesian approach from Reid et al. (2016, ApJ, 823, 77; 2019, ApJ, 885, 131), which is based on our most precise knowledge at present about the structure and kinematics of the Milky Way, to obtain the current best assessment of the Galactic distribution of 13CO from the Galactic Ring Survey. We performed two different distance estimates that either included (Run A) or excluded (Run B) a model for Galactic features, such as spiral arms or spurs. We also included a prior for the solution of the kinematic distance ambiguity that was determined from a compilation of literature distances and an assumed size-linewidth relationship. Even though the two distance runs show strong differences due to the prior for Galactic features for Run A and larger uncertainties due to kinematic distances in Run B, the majority of their distance results are consistent with each other within the uncertainties. We find that the fraction of 13CO emission associated with spiral arm features ranges from 76 to 84% between the two distance runs. The vertical distribution of the gas is concentrated around the Galactic midplane, showing full-width at half-maximum values of ~75 pc. We do not find any significant difference between gas emission properties associated with spiral arm and interarm features. In particular, the distribution of velocity dispersion values of gas emission in spurs and spiral arms is very similar. We detect a trend of higher velocity dispersion values with increasing heliocentric distance, which we, however, attribute to beam averaging effects caused by differences in spatial resolution. We argue that the true distribution of the gas emission is likely more similar to a combination of the two distance results discussed, and we highlight the importance of using complementary distance estimations to safeguard against the pitfalls of any single approach. We conclude that the methodology presented in this work is a promising way to determine distances to gas emission features in Galactic plane surveys.

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Section: Comparison With Previous Resultscontrasting

confidence: 52%

Section: A3 Literature Results Used In the Kda Priormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Autonomous Gaussian decomposition of the Galactic Ring Survey

Riener

Kainulainen

Henshaw

et al. 2020

A&A

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Automatically verifying molecular clumps based on supervised learning

Long,

Zheng,

Huang

et al. 2024

New Astronomy

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Bird’s eye view of molecular clouds in the Milky Way

Spilker

Kainulainen

Orkisz

2021

A&A

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Context. Describing how the properties of the interstellar medium are combined across various size scales is crucial for understanding star formation scaling laws and connecting Galactic and extragalactic data of molecular clouds. Aims. We describe how the statistical structure of the clouds and its connection to star formation changes from sub-parsec to kiloparsec scales in a complete region within the Milky Way disk. Methods. We built a census of molecular clouds within 2 kpc from the Sun using data from the literature. We examined the dust-based column density probability distributions (N-PDFs) of the clouds and their relation to star formation as traced by young stellar objects (YSOs). We then examined our survey region from the outside, within apertures of varying sizes, and describe how the N-PDFs and their relation to star formation changes with the size scale. Results. We present a census of the molecular clouds within 2 kpc distance, including 72 clouds and YSO counts for 44 of them. The N-PDFs of the clouds are not well described by any single simple model; use of any single model may bias the interpretation of the N-PDFs. The top-heaviness of the N-PDFs correlates with star formation activity, and the correlation changes with Galactic environment (spiral- and inter-arm regions). We find that the density contrast of clouds may be more intimately linked to star formation than the dense gas mass fraction. The aperture-averaged N-PDFs vary with the size scale and are more top-heavy for larger apertures. The top-heaviness of the aperture N-PDFs correlates with star formation activity up to roughly 0.5 kpc, depending on the environment. Our results suggest that the relations between cloud structure and star formation are environment specific and best captured by relative quantities (e.g. the density contrast). Finally, we show that the density structures of individual clouds give rise to a kiloparsec-scale Kennicutt-Schmidt relation as a combination of sampling effects and blending of different galactic environments.

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CHIMPS: physical properties of molecular clumps across the inner Galaxy

Cited by 50 publications

References 81 publications

Autonomous Gaussian decomposition of the Galactic Ring Survey

Autonomous Gaussian decomposition of the Galactic Ring Survey

Automatically verifying molecular clumps based on supervised learning

Bird’s eye view of molecular clouds in the Milky Way

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