A large catalogue of molecular clouds with accurate distances within 4 kpc of the Galactic disc Free

ABSTRACT

1 INTRODUCTION

Most molecular gas in our Galaxy is contained in molecular clouds, where the star formation takes places (Blitz & Williams 1999). The formation of molecular clouds and the birth of stars are key processes in the life cycle of galaxies. Understanding the physical properties of molecular clouds is thus of great importance. Distance of the molecular clouds is fundamental to estimate from observations physical properties including size and mass, etc. Moreover, mapping the spatial distribution of molecular clouds at large scale plays a crucial role for understanding their formation and evolution, and their role in star formation (Li et al. 2013, 2016; Goodman et al. 2014; Wang et al. 2015). However, building a large sample of molecular clouds with accurate distance estimates is a difficult task.

The traditional method used to trace molecular clouds and derive their basic physical properties uses CO observations. H₂, the dominant species in a molecular cloud, is hard to excite and observe under typical conditions of dark, cold clouds. In comparison, heteronuclear diatomic molecule CO, the second most abundant species in a molecular cloud, can be easily excited and observed, and has become the primary tracer of molecular clouds. There are now many large-area CO surveys of the Milky Way and a large number of molecular clouds have already been identified (e.g. Magnani, Blitz & Mundy 1985; May, Alvarez & Bronfman 1997; Dame, Hartmann & Thaddeus 2001). Distances of those clouds have been derived from the Galactic rotation curve (e.g. Brand et al. 1994; May et al. 1997; Nakagawa et al. 2005; Roman-Duval et al. 2009; García et al. 2014; Miville-Deschênes, Murray & Lee 2017). However, those kinematic distances suffer from large uncertainties due to the difficulties of determining an accurate rotation curve and the influence of the peculiar velocities and the non-circular motions, as well as the well-known near-far ambiguity – where one velocity can be related to two distances when a cloud is distributed in the inner Galaxy.

A complementary approach to probe the properties of molecular clouds is to trace the molecular gas by dust observations, specifically via the optical/near-infrared (IR) dust extinction measurements (Goodman, Pineda & Schnee 2009; Chen et al. 2017). Based on the 2D Galactic extinction maps, Dobashi (2011) have identified more than 7000 molecular clouds. But they were unable to obtain distance informations for those molecular clouds given the 2D nature of the extinction maps. Marshall, Joncas & Jones (2009) have obtained distances for over 1000 clouds by analysing the 3D extinction maps obtained by comparing the observed colour distributions of Galactic giant stars with those predicted by the Galactic model. Their distance estimates have uncertainties about 0.5–1 kpc. Lada, Lombardi & Alves (2009) and Lombardi, Alves & Lada (2011) have obtained distance estimates for a number of clouds by comparing the number of unextinguished stars, presumably located in front of the clouds, with the predictions of the Galactic model. Recently, due to the availability of large amounts of multiband photometric and astrometric data, we are now able to obtain values of distance and dust extinction for tens of millions individual stars (Chen et al. 2014, 2019b; Green et al. 2015; Lallement et al. 2019). Based on PanSTARRS-1 data, Schlafly et al. (2014) present a catalogue of distances to molecular clouds selected from Magnani et al. (1985) and Dame et al. (2001) by the 3D extinction mapping method. The distance estimates of Schlafly et al. (2014) have uncertainties of about 10 per cent. Using a similar technique, Zucker et al. (2019) obtain distances to dozens of local molecular clouds using a combination of optical and near-IR photometry and the Gaia Data Release 2 (Gaia DR2; Gaia Collaboration 2018) parallaxes (Lindegren et al. 2018). Most recently, Yan et al. (2019) present a catalogue of distances to molecular clouds at high Galactic latitudes based on estimates of parallax and extinction from Gaia DR2. Benefitting from the large numbers of stars for the individual molecular clouds and the robust estimates of the stellar distances from Gaia DR2, the errors of distances obtained by Zucker et al. (2019) and Yan et al. (2019) are typically only about 5 per cent.

Most of the molecular clouds are located in the Galactic disc, especially at Galactic low latitudes (|b| < 10°). However, hitherto only a few local well-studied molecular clouds at low latitudes, such as the California and Pipe Nebula, have accurate distance estimates based on the 3D dust extinction mapping method. In the Galactic plane, there are numerous high-density clouds and they tend to overlap with each other along the sightlines. This poses extra difficulty to isolate the individual molecular clouds from their foreground or background ones. In this work, we identify and isolate the molecular clouds in the Galactic plane by applying a hierarchical cluster analysis technique to the 3D colour excess maps of the Galactic disc presented by Chen et al. (2019b, hereafter Paper I). For each cloud, we select stars in the overlapping directions and fit the colour excess and distance relation for them. The fit gives the distance to the cloud.

This paper is part of an ongoing project to study the interstellar dust and the Milky Way structure. The basic data are from Paper I that presents 3D interstellar dust reddening maps of the Galactic plane (Galactic longitude 0° < l < 360° and latitude |b| < 10°) in three colour excesses, E(G − K_S), E(G_BP − G_RP), and E(H − K_S). In this work, we will present a large catalogue of 567 molecular clouds in the Galactic disc with accurate determination of distances and other physical parameters. With the data, we will carry out a detailed analysis of the 3D motions of the molecular clouds and study the kinematics of the Galactic spiral structure (Li et al., in preparation).

The paper is structured as following. In Section 2, we describe the data. In Section 3, we introduce our method for isolating the molecular clouds in the disc and determining the distances to them. We present our results in Section 4 and discuss them in Section 5. We summarize in Section 6.

2 DATA

In Paper I, we have calculated the values of colour excesses E(G − K_S), E(G_BP − G_RP), and E(H − K_S) of more than 56 million stars located at low Galactic latitudes (|b| < 10°). In doing so, we combined the high-quality optical photometry from Gaia DR2 (Gaia Collaboration 2018) with the near-IR photometry from the Two Micron All Sky Survey (2MASS; Skrutskie et al. 2006) and the Wide-Field Infrared Survey Explorer (Wright et al. 2010). The machine learning algorithm random forest Regression was applied to the photometric data to obtain values of colour excesses of the individual stars, using a training data sample constructed from spectroscopic surveys. The typical uncertainty of the resulted colour excess values was about 0.07 mag in E(B − V) (Paper I). Distances estimated by Bailer-Jones et al. (2018), who transfer the Gaia DR2 parallaxes to distances using a simple Bayesian approach, were adopted for the stars. A simple cut of the Gaia DR2 parallax uncertainties of smaller than 20 per cent was applied to exclude the stars with large distance errors. The analysis yielded a sample, denoted ‘C19 Sample’, that consists of over 32 million stars with estimates of distances and colour excesses. Based on the C19 Sample, Paper I presents 3D maps of colour excess E(G_BP − G_RP) of the Galactic disc. The maps have an angular resolution of 6 arcmin and cover the entire Galactic disc. The typical depth limit of the maps is about 4 kpc from the Sun.

3 METHOD

As the first step of our method, the 3D colour excess maps from Paper I are used to isolate the individual molecular clouds. We adopt a cloud identification method that uses a clustering hierarchical algorithm to identify coherent structures in the position (l, b) and distance (d) space at the same time. We use the python program dendrogram (Rosolowsky et al. 2008) to identify molecular clouds in the 3D data cube of the E(G_BP − G_RP) maps. dendrograms are tree representations of the hierarchical structure of nested isosurfaces in 3D line data cubes. The algorithm has three input parameters: (i) ‘min_value’, the minimum value used to mask any structure that peaks below it; (ii) ‘min_delta’, the minimum significance for structures used to exclude any local maxima identified because of the noise; and (iii) ‘min_npix’, the minimum number of pixels that a structure should contain. In this work, we test with different parameters to optimize our selection. Finally, we set min_value  = 0.05 mag kpc⁻¹, min_delta  = 0.06 mag kpc⁻¹, and min_npix  = 20. The ‘leaves’ of a dendrogram correspond to the regions that have density enhancements, i.e. the individual molecular clouds in our case.

For each cloud, the scale factors f₁ and f₂, and the width and distance of the cloud δd and d₀ are free parameters to fit. There could be more than one dust clouds for a given sightline. To avoid contamination by other clouds, we fit the colour excess profile only in a limited distance range, d_min − 0.2 kpc < d < d_max + 0.2 kpc. The values of colour excess of the individual stars in the distance range are then fitted with the colour excess model described above with the IDL program mpfit (Markwardt 2009), which is implementation of the Levenberg–Marquardt least-squares optimization algorithm (Moré 1978). No priors are adopted for the fitting. The uncertainties of the resultant parameters are derived using a Monte Carlo method. For each molecular cloud, we randomly generate 300 samples of stars taking into account their distance and colour excess uncertainties. We apply the fitting algorithm and derive the parameters for all samples. The results for each of the parameter follow a Gaussian distribution. The dispersion of the Gaussian distribution is taken as the error of the corresponding parameter.

4 RESULTS

In this work, we have identified 567 molecular clouds with program dendrogram. The distribution of the clouds, catalogued in Table 1, in Galactic coordinates l and b is presented in Fig. 1. The typical solid angle subtended by a cloud is about 1.5 deg². Typically, there are ∼4500 stars along the sightlines towards a cloud in the C19 Sample.

Figure 1.

Distributions of the molecular clouds identified in this work in the Galactic coordinates. The background three-colour composite images illustrate the 3D distributions of dust in the Galactic disc from Paper I. The red, green, and blue scales show the distributions of the colour excess δE(G_BP − G_RP) in distance slices 0–1000 pc, 1000–2000 pc, and 2000–5000 pc from the Sun, respectively. The white polygons mark the l and b boundaries of the individual molecular clouds identified in this work.

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4.1 Distances of the molecular clouds

Fig. 2 shows the results of our colour excess profile fitting procedure for three example molecular clouds. In general, the values of colour excess E(G_BP − G_RP) of stars in individual distance bins show significant dispersions, as those stars cover quite a large area (∼1–2 deg²). However, the ‘jumps’ in colour excess values produced by the molecular clouds in the corresponding distance ranges given by program dendrogram are clearly visible. They are nicely fitted by our colour excess profiles.

Figure 2.

Distances to clouds No. 61 (upper panels), 318 (middle panels), and 539 (bottom panels). In the left-hand panels, the red, green and blue scales show the distributions of the colour excess δE(G_BP − G_RP) in distance slices 0–1000 pc, 1000–2000 pc, and 2000–5000 pc from the Sun, respectively. The white polygons show the region of sky of the clouds and from which the stars used in the distance determination are drawn. In the right-hand panels, the values of colour excess E(G_BP − G_RP) of the selected stars are plotted against distances of the stars. The rectangles show the best-fitting colour excess profiles. In each panel, the cyan rectangle represents the width of the cloud (δd) and the blue rectangles show the 1σ regions of the foreground and background colour excesses of the cloud. The red-dashed lines show the distances of the clouds.

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The panels in the first row of Fig. 2 show the fitting analysis of a nearby molecular cloud with distance d of 127 pc from the Sun. Due to the saturation of the photometric data used in Paper I, there are only a few stars in the C19 Sample that are located in front of the cloud. The quality of colour excess profile fit for the cloud is relatively poor compared to that of more distant clouds.

The panels in the second and third rows of Fig. 2 show examples of two clouds located, respectively, at distances 659 and 2453 pc from the Sun. Compared to the nearby molecular cloud, the distance ranges of the colour excess jumps of more distant molecular clouds are larger. As a result, more distant clouds have larger ‘widths’ (δd). This is mainly caused by the larger distance errors of stars at further distances. If one assumes that the molecular clouds are spherical, the intrinsic widths of the clouds range between ∼1 and 100 pc (see Section 4.2). On the other hand, the typical uncertainties of the Gaia parallaxes for stars at distances of 2 kpc are ∼10 per cent, i.e. ∼200 pc, which is twice of the upper limit of the intrinsic widths of the clouds. Therefore, the resulted values of width δd for distant clouds are mainly contributed by the distance errors of the individual stars. However, benefitting from the large numbers of stars for the individual clouds, the large distance uncertainties do not have significant impacts on the distance determinations of the clouds.

In directions towards some molecular clouds, we are able to see more than one colour excess jump. The additional jumps are produced by the foreground or background clouds. With the distance ranges provided by dendrogram, we are able to exclude the contamination of those foreground or background clouds and obtain accurate distances for the individual identified clouds.

For each molecular cloud identified in this work, we have made figures analogous to Fig. 2. They are available online.² The best-fittng values and uncertainties of distances d₀ and width δd, and the averaged colour excess |$\overline{E(G_{\rm BP}-G_{\rm RP})}$| of the individual catalogued clouds are listed in Table 1. Fig. 3 plots the centre longitude l of all the molecular clouds against their distances d₀. The associated statistical distance uncertainties and the estimated widths (depths) of the clouds (δd) are also overplotted. The cloud range in distances from d₀ ≈ 30 to ∼4000 pc. The catalogue should be complete between 1 and 3 kpc. For distances larger than 3 kpc, our sample is incomplete due to the limited depth of the adopted 3D colour excess maps, while for distances closer than 1 kpc, it is incomplete mainly due to the incomplete coverage of Galactic latitudes of the data set. In addition, we note that the far-away clouds would appear be stretched due to the relatively large distance errors. Their observed differential colour excesses [dE(G_BP − G_RP)/ds, where s is the distance] are actually smaller than the intrinsic values. Thus, the far-away clouds with low dust density could be under our detection threshold. At larger distances, our selection procedure might have missed those low-density clouds or the low-density parts of the clouds. Owing to the accurate distances of stars yielded by the Gaia parallaxes and the large numbers of stars available to trace the individual clouds, we have achieved a very high precision for our distance estimates of the clouds. Most of the clouds in our catalogue have relative distance uncertainties smaller than 5 per cent. The typical ‘width (depth)’ of the catalogued clouds is about 18 per cent of the distance values, largely caused by the distance errors of the individual stars used to trace the clouds.

Figure 3.

Derived cloud distances plotted against their Galactic longitudes. The red and blue error bars show the distance uncertainties and the derived width/depth (δd) of the clouds, respectively. Also, shown to the right of the panel is the distribution of the estimated total masses of the molecular clouds in the individual distance bins of width 0.2 kpc.

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4.2 Physical properties

The derived physical properties, such as the physical radius r, mass M, and surface mass density Σ, of the molecular clouds are also listed in Table 1. Fig. 4 plots the histograms of those properties. The radii of the clouds range between 0.2 and 90 pc with a median value of ∼8 pc, the masses between 5 and 700 000 M_⊙ with a median value of ∼10 000 M_⊙ and the surface mass densities between 10 and 125 M_⊙ pc² with a median value of ∼50 M_⊙ pc².

Figure 4.

The histograms of the physical properties radii (upper panel), masses (middle panel), and surface mass densities (bottom panel) of the molecular clouds catalogued in this work (the red lines), and those in Roman-Duval et al. (2010; the blue lines) and Miville-Deschênes et al. (2017; the black lines).

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The distributions of the corresponding properties of clouds studied by Roman-Duval et al. (2010) and Miville-Deschênes et al. (2017) are also overplotted in Fig. 4 for comparison. Roman-Duval et al. (2010) have derived physical properties of 580 molecular clouds based on the CO observations of the University of Massachusetts-Stony Brook and Galactic Ring surveys. Miville-Deschênes et al. (2017) have isolated 8107 molecular clouds from the CO observations of Dame et al. (2001) and derived their physical properties. The molecular clouds of Roman-Duval et al. (2010) and Miville-Deschênes et al. (2017) cover disc regions that are at much further distances from the Sun than those catalogued here. In addition, their physical properties are obtained from data with methods much different from ours. Nevertheless, the resultant distributions of the physical radii and masses of the molecular clouds are very similar, suggesting that the structures identified in this work are essentially the same type of object as those identified in those previous studies.

Fig. 5 shows the correlation between the radii and masses of the molecular clouds. The radii and masses of our molecular clouds are tightly correlated by a power law, M = 156.6R^1.96. The exponent index 1.96, which corresponds to a typical surface density of ∼50 M_⊙ pc², is slightly smaller than that found by Roman-Duval et al. (2010) but consistent with that of Miville-Deschênes et al. (2017). This limiting surface density is largely caused by the fact that we have imposed a limiting extinction to our sample sources (Section 3).

Figure 5.

The masses plot against the radii for the molecular clouds in this work (the red circles), that of Roman-Duval et al. (2010; the blue squares) and Miville-Deschênes et al. (2017; the black dots). The blue line is the best-fitting correlation of the mass of the molecular clouds in our work and their radii.

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The estimation of cloud masses and radii depends on how the cloud boundaries are defined. The differences in cloud mass-size distributions reported in different work are most likely caused by the different criteria used to define the clouds.

5 DISCUSSION

5.1 Comparison with previous work

The unique catalogue presented in this work is one of the largest homogeneous catalogues of molecular clouds with accurate distance estimates. The clouds span in distances from ∼30 to ∼4000 pc, with typical uncertainties of less than 5 per cent. To verify the robustness of our distance estimates, we have selected some well-studied giant molecular clouds and collect their distance estimates from the literature. The results are listed in Table 2. Overall, our values agree well with those from the literature.

For some nearby giant molecular cloud complexes, such as the California, Pipe Nebula, CMA OB1, and Orion Lam, we have isolated more than one clouds in each of them. The resulted distance estimates of the individual clouds are consistent with each other within 3σ. There are also some molecular clouds that have extended to the medium or high Galactic latitudes, such as the Orion Lam, Mon R2, Hercules, etc. We have been only able to identify parts of them, limited by the latitude coverage of our data (b < 10°). Nevertheless, our estimated distances are in good agreement with those in the literature. The Cepheus Flare, for example, is a structure complex that contains at least two components (Kun, Kiss & Balog 2008; Schlafly et al. 2014). As the footprint of our data is limited to Galactic latitudes smaller than 10°, we are only able to identified parts of its southern component, named cloud No. 353 in this work. It is centred at l  = 106.260° and b  = 9.687° and have a distance of 1047.8 ± 24.7 pc. Literature estimates of distance for this component are 900 ± 90 pc (Schlafly et al. 2014), 923 ± 47 pc (Zucker et al. 2019), and 1043|$^{+6}_{-7}$| pc (Yan et al. 2019), all in good agreement with our current result. The dispersion of differences between our distance estimates and those from Zucker et al. (2019) and Yan et al. (2019) is about 5.6 per cent. Note that all the cloud distances here are derived from Gaia DR2 parallaxes.

We also compare the spatial distribution of our molecular clouds to the 3D dust distribution of the Galactic plane from Lallement et al. (2019). Based on the photometric data of Gaia DR2 and 2MASS, and the parallaxes of Gaia DR2, Lallement et al. (2019) present the 3D distribution of the interstellar dust in a volume of 6 × 6 × 0.8 kpc³ around the Sun with a hierarchical inversion algorithm. The comparison is shown in Fig. 6. The spatial distribution of the molecular clouds catalogued here is in excellent agreement with the dust distribution of Lallement et al. (2019). The good agreement validates the robustness of methods used in both papers.

Figure 6.

Spatial distribution of the molecular clouds identified and catalogued in this work (the circles) in the X–Y plane, plotted over the distribution of the Galactic dust of disc vertical height |Z| < 0.4 kpc (the blue scales; Lallement et al. 2019). The sizes of the circles are proportional to the physic sizes of the individual clouds and the error bars indicate their distance uncertainties. The Sun, assumed to be at the position of (X, Y, Z)  = (−8.34, 0, 0) kpc, is located at the centre of the plot.

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5.2 The Galactic spiral structure as traced by the molecular clouds

As Figs 3 and 6 show, the molecular clouds in our catalogue trace clearly the large-scale structure of the Galactic disc, i.e. the spiral arms. The gaps between the different arms are quite visible.

In Fig. 7, we plot the spatial distribution of the molecular clouds in the X–Y plane. The distributions of masers (table 2 of Xu et al. 2018) and young OB stars and candidates (Chen et al. 2019a) are overplotted in the diagram. Chen et al. (2019a) have estimated structure parameters of the Scutum, Sagittarius, Local and Perseus Arms based on their sample of O and early B-type stars and the maser sample of Xu et al. (2018). Overall, the molecular clouds catalogued here are very likely to be spatially associated with the arms delineated by Chen et al. (2019a).

Figure 7.

Spatial distribution of the identified molecular clouds in the X–Y plane. The sizes of the circles are proportional to the physic sizes of the individual clouds and the error bars indicate their distance uncertainties. The solid orange, cyan, blue, and pink lines delineate the best-fitting spiral arm models of the Sagittarius, Local and Perseus Arms presented in Chen et al. (2019a). The circles of the aforementioned colours are the masers from Xu, Hou & Wu (2018) that are probably associated with the individual Arms. The grey background image represents the spatial distribution of the OB stars from Chen et al. (2019a). The green-, pink-, and blue-dashed lines mark, respectively, a possible spur ‘Lower Sagittarius–Carina Spur’, the ‘split’ found by Lallement et al. (2019), and the spur identified by Xu et al. (2016). The three black-dashed circles mark the positions of three possible ‘hole’ patterns in the Sagittarius and Perseus Arms identified in Chen et al. (2019a). The Sun, assumed to be at the position of (X, Y, Z)  = (−8.34, 0, 0) kpc, is located at the centre of the plot. The directions of l  = 0°, 45°, 90°, 135°, 180°, 225°, 270°, and 315° are also marked in the plot.

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The Perseus Arm is well traced by the molecular clouds, the masers and the OB stars in the outer disc, for longitudes between 90° and 230°. The Perseus Arm is quite a diffuse arm. Based on the spatial distribution of the OB stars and masers, Chen et al. (2019a) identify two possible ‘hole’ patterns within the Arm with no masers and few OB stars. The molecular clouds identified here around the two ‘holes’ are also fragmented, albeit with a different pattern. A significant fraction of the clouds concentrated in the direction of the Galactic anticentre (l ∼ 180°), where the masers and OB stars also clump. Although several molecular clouds are visible in the two ‘holes’ reported by Chen et al. (2019a), the ‘holes’ are less populated by the clouds than in other areas.

Two significant cavities are visible in the Solar neighbourhood. Cavity 1 (Chen et al. 2014; Lallement et al. 2019) falls in the direction l ∼ 240° and Cavity 2, which is smaller, in l ∼ 60°. The model line of the Local arm, derived from the OB stars and masers, goes along the upper boundary of Cavity 2, and through the centre of Cavity 1. The lower boundaries of the two cavities seem to be the foreground structure of the spur identified by Xu et al. (2016) that connects the Local and the Sagittarius Arms at a pitch angle of ∼18° (the blue-dashed line in Fig. 7). On the other hand, those nearby molecular clouds around l ∼ 45° seem also to be the foreground clouds of the so-called ‘split’ (Lallement et al. 2019) that connects the Local and the Sagittarius Arms at a pitch angle of ∼48° (the pink-dashed line in Fig. 7).

Due to the significant amount of foreground dust extinction, we have not been able to identify the molecular clouds of the Sagittarius Arm in the direction of l between 30° and 45°. The molecular clouds located in the directions of l ∼ 15° (X ∼ −6.8 kpc and Y ∼ 0.5 kpc) and l ∼ 285° (X ∼ −7.5 kpc and Y ∼ −2.5 kpc) seems to be parts of the Sagittarius Arm. Similar to the distribution of OB stars, no molecular clouds are found in the ‘hole’ of the Sagittarius Arm identified by Chen et al. (2019a). Another significant feature is a structure of ∼3 kpc length that connects the the Local and the Sagittarius Arms at a pitch angle of ∼34° (the green-dashed line in Fig. 7). This structure is apparent in the previous 3D extinction maps such as Chen et al. (2019b) and Lallement et al. (2019). It was treated as a cloud complex and was labelled as ‘Lower Sagittarius–Carina’ in Lallement et al. (2019). However, it could be a new spur of the Galactic spiral structure and we call it ‘Lower Sagittarius–Carina Spur’. Chen et al. (2019c) have reported a massive star-forming region G352.630–1.067 and suggest that it may be located in a spur extending from the Sagittarius Arm close to the direction of Galactic centre. The distance of G352.630–1.067 is 0.69 kpc. It is much closer than the new spur found in this work that have a distance of about 1.1 kpc in the direction. A detailed analysis of this new spur will be presented in a future work (Li et al., in preparation).

6 CONCLUSION

In this paper, we have presented a new, large, and homogeneous catalogue of 567 molecular clouds within 4 kpc from the Sun at low Galactic latitudes (|b| < 10°) using the data presented in Paper I. The molecular clouds are identified by a dendrogram analysis of the 3D colour excess maps of the Galactic disc. Based on the previous determinations of extinction values and distances of over 32 million stars, we have derived accurate distances of those molecular clouds by analysing the colour excess and distance relations along the sightlines of the individual clouds. The typical errors of the distances are less than 5 per cent. As far as we know, the resulted catalogue is the first large catalogue of molecular clouds with directly measured distances in the Galactic plane.

We have also measured the areas, the linear equivalent radii, the masses, and the surface mass densities of the catalogued molecular clouds. A tight power-law correlation of index 1.96, is found between the radii and masses.

We have explored the distribution of our clouds in the Galactic disc, and studied the connection between the dust distribution and the spiral arms. Broadly speaking, the molecular clouds are found to be spatially associated with the Galactic spiral arm models delineated in the previous work (Chen et al. 2019a), where on smaller scales, spur-like structures are not uncommon. In this respect, we have identified a possible spur, the ‘Lower Sagittarius–Carina Spur’, with a pitch angle of about 34° that connects the Local and the Sagittarius Arms in the fourth quadrant.

SUPPORTING INFORMATION

Table 1. Catalogue of molecular clouds.

Please note: Oxford University Press is not responsible for the content or functionality of any supporting materials supplied by the authors. Any queries (other than missing material) should be directed to the corresponding author for the article.

ACKNOWLEDGEMENTS

We want to thank our anonymous referee for the helpful comments. This work is partially supported by National Natural Science Foundation of China 11803029, U1531244, 11833006, and U1731308 and Yunnan University grant No. C176220100007. HBY is supported by NSFC grant No. 11603002 and Beijing Normal University grant No. 310232102. This research has used astrodendro, a python package to compute dendrograms of Astronomical data (http://www.dendrograms.org/).

This work has used data products from the Guoshoujing Telescope (the Large Sky Area Multi-Object Fibre Spectroscopic Telescope, LAMOST). LAMOST is a National Major Scientific Project built by the Chinese Academy of Sciences. Funding for the project has been provided by the National Development and Reform Commission. LAMOST is operated and managed by the National Astronomical Observatories, Chinese Academy of Sciences.

This work presents results from the European Space Agency space mission Gaia.Gaia data are being processed by the Gaia Data Processing and Analysis Consortium (DPAC). Funding for the DPAC is provided by national institutions, in particular the institutions participating in the Gaia MultiLateral Agreement. The Gaia mission website is https://www.cosmos.esa.int/gaia. The Gaia archive website is https://archives.esac.esa.int/gaia.

Footnotes

REFERENCES

Author notes