© The Authors 2024

1. Introduction

The Large Magellanic Cloud (LMC) is a unique laboratory for extragalactic studies of star formation. Owing to the proximity and nearly face-on orientation of the galaxy, it is possible to study star formation in it at a wide range of spatial scales across the entire electromagnetic spectrum.

One of the most prominent objects in the LMC is the active star-forming region 30 Doradus (hereafter 30Dor). 30Dor is the brightest HII region in the Local Group. It hosts the star cluster NGC 2070, in the centre of which resides the concentration of stars R136, which has a central density of at least 1.5 × 10⁴ M_⊙ pc⁻³ (Selman & Melnick 2013). Based on the data of the VLT-Flames Tarantula Survey (Evans et al. 2011), the 30Dor region contains ∼1000 massive stars¹ (Crowther & Walborn 2011; Evans et al. 2011, 2015; Doran et al. 2013; Walborn et al. 2014); in particular, several of the most massive stars known, with masses up to 200–300 M_⊙ (Crowther et al. 2010, 2016; Hainich et al. 2014; Bestenlehner et al. 2020; Kalari et al. 2022; Brands et al. 2022).

Stars inject energy and momentum and deposit metals into their surroundings. This phenomenon is called stellar feedback and it plays an important role in the life cycle of molecular clouds as well as star formation (e.g. Dobbs et al. 2014; Chevance et al. 2020b, 2022). Feedback disrupts and displaces clouds, which leads to suppression of star formation (negative feedback; e.g. Körtgen et al. 2016; Kruijssen et al. 2019; Chevance et al. 2020a). On the other hand, due to feedback, the density and turbulence of the gas in the clouds can increase, triggering star formation (positive feedback; e.g. Whitworth et al. 1994; Koenig et al. 2012; Cosentino et al. 2022). Several modes of feedback are known to exist, including protostellar outflows, photoionization (warm HII), direct radiation, indirect radiation (re-radiated by dust), stellar winds, and supernovae (see e.g. Krumholz et al. 2019; Rosen et al. 2020; Chevance et al. 2022, for a review). Massive stars generate all types of feedback that extend throughout the surrounding interstellar medium. Moreover, the feedback mechanisms other than protostellar outflows are dominated by massive stars. From this perspective, 30Dor is a perfect target for studying stellar feedback in general and its effect on molecular clouds in particular.

There are several works on feedback in 30Dor. These studies combine theoretical modelling and usage of multi-wavelength (radio, infrared, optical, UV, and X-ray) data (Lopez et al. 2011, 2014), stellar populations (Doran et al. 2013; Bestenlehner et al. 2020), emission-line ratios (Pellegrini et al. 2010, 2011), and CO spectral line energy distributions (Lee et al. 2019) to match and compare the predictions of the models. The study by Tram et al. (2023), who find evidence for expanding shells in the kinematics of 30Dor correlated with the magnetic fields of the region, is also to be mentioned. At the same time, high-resolution studies of the molecular cloud population in 30Dor are restricted to relatively small regions observed in the J = 2 → 1 lines of ¹²CO, ¹³CO, and C¹⁸O with the Atacama Large (sub)Millimeter Array (ALMA). These include the 12 × 12 pc region located northeast of R136 from Indebetouw et al. (2013, 2020) and the ∼60  ×  90 pc mosaic that is presented in Wong et al. (2022). Nevertheless, these high-resolution ALMA data sets cover just the direct vicinity of 30Dor and do not allow for comparison of its clouds to the full population of the LMC. To date, the most complete census of the LMC clouds is the MAGMA survey (Wong et al. 2011, 2017). However, its patchy coverage (3.6 sq. deg. in total) and the spatial resolution limit of ∼11 pc impose limitations on building statistically complete samples of clouds for a detailed region-to-region analysis.

The new ongoing APEX LMC Legacy Survey aims to address the issues above. Its resolution allows us to study clouds with a linear size as small as ∼5 pc. Moreover, its coverage (see Fig. 1) is the most homogeneous and complete ever obtained for the LMC in a CO line.

Fig. 1. Large Magellanic Cloud. The underlying image is the Hα MCELS map (Smith & MCELS Team 1998). Panel a shows the coverage of the ongoing APEX CO line Legacy Survey (white line) analysed in this work and (in yellow-green) the contours of the CO moment-zero map, with the level corresponding to the signal-to-noise threshold of S/N = 2 in the masked CO data. The moment-zero contours overlapping with Hα-bright regions (see Sect. 3) are highlighted in blue. The 30Dor region is marked by the pink circle with a radius of 100 pc that is centred on the position of R136. Panel b is a zoom into the 30Dor region. The positions of the molecular clouds are shown by X marks.

n this paper, we study the effect of large-scale feedback on the properties of molecular clouds in the 30Dor region of the LMC. Our analysis relies on a comparison of the clouds in 30Dor and the rest of the LMC within the current coverage of the survey. Such analysis in the LMC is done for the first time. This paper is organised as follows: we briefly introduce our CO data in Sect. 2, proceed to the methods used and results obtained in Sect. 3, and finally discuss and summarise our findings in Sects. 4 and 5.

2. Observations and data

Our study is based on the observations of the LMC in the ¹²CO(3 − 2) line with the Large APEX sub-Millimetre Array (LAsMA) receiver of the Atacama Pathfinder Experiment (APEX) telescope (Güsten et al. 2006). LAsMA is a 7-pixel dual-side band receiver, which allows us to observe the ¹²CO(3 − 2) and ¹³CO(3 − 2) lines simultaneously. The observations are currently ongoing and are in the framework of the new APEX LMC Legacy CO survey (PI: A. Weiss). In total, the survey will cover ∼17.4 sq. deg. (∼13.3 kpc²) of the LMC disc, providing an unbiased view of the full molecular gas distribution in the galaxy. The data used in this work cover 13.8 sq. deg. (10.5 kpc²) of the galaxy disc, which corresponds to ∼80 % of the complete coverage planned. The coverage analysed in this work is shown in Fig. 1. The data have an angular resolution of 20″ at 345.8 GHz, the frequency of the ¹²CO(3 − 2) line, which corresponds to a spatial resolution of 5 pc. The original bandwidth of the data is 4 GHz, which corresponds to an LSR-velocity range from −160 km s⁻¹ to 3280 km s⁻¹. However, we cut this spectral coverage to the velocities from 140.5 km s⁻¹ to 340.0 km s⁻¹, selecting the velocity range with emission and ∼150 adjacent emission-free channels in our data cube. The emission-free channels were used to estimate the noise. The main beam surface brightness sensitivity of our data is ∼150 mK at a 0.5 km s⁻¹ spectral resolution, with a spread in values of ∼25 mK as measured by the standard deviation. The RMS noise distribution for our data cube is shown in the Appendix. The sensitivity above corresponds to a 4.5σ point source line-luminosity limit of $L_{\text{CO}(\text{3-2})}^{\prime }\approx 20\text{K}\text{km}{\text{s}}^{\text{-1}}{\text{pc}}^{\text{2}}$. The survey has mapped for the first time the entire surroundings of 30Dor at a sub-arcmin (20″) spatial resolution in the CO line. More details about the survey will be given in forthcoming papers.

3. Method and results

In order to extract the signal from the data, we masked it using the dilated masking technique discussed in Rosolowsky & Leroy (2006), Rosolowsky et al. (2021). We derived the positions of the clouds and estimated their properties following Solomon et al. (1987), Rosolowsky & Leroy (2006), Colombo et al. (2019) and analysed the data using the astronomical dendrograms². We define clouds as elements of the trunk of the resulting dendrogram, that is independent structures that do not have parent structures. In essence, the trunk of a dendrogram is a set of isolated and connected regions in the position-position-velocity (PPV) space with emission above a certain signal-to-noise level. When applicable, the estimated parameters were corrected for the beam convolution and uncertainties of the cloud properties were calculated. In the Appendix, we give details on the data preparation (Appendix A), parameter estimation, and their corrections (Appendix B), as well as uncertainties (Appendix C). Throughout the analysis, we adopted a distance to the LMC of D_LMC = 50 kpc (Pietrzyński et al. 2019).

3.1. Comparison of the 30Dor and field clouds

Feedback affects molecular clouds structurally and may alter their kinematics (see Sect. 1 and references therein as well as e.g. Dale et al. 2012; Krumholz et al. 2014; Shima et al. 2017; Mazumdar et al. 2021; Melnick et al. 2021). For this reason, we have compared the structural and kinematic properties of the clouds of 30Dor with those of the clouds in the rest of the LMC within the analysed coverage (see Fig. 1). Our data in the ¹²CO(3–2) line allow us to estimate the line luminosity, line-of-sight velocity, relative distance between the clouds in the position-position (PP) plane, their size, virial mass, and linewidth. In order to distinguish between the clouds of 30Dor and all the other ones, we set a circular aperture around the centre of 30Dor. For the centre, we adopted the position of R136: RA(J2000) = 05^h38^m42^s.4, Dec(J2000) = −69°06′03″.4 (Høg et al. 2000). The clouds inside and outside the aperture are hereafter referred to as the 30Dor and field clouds, respectively. As a standard aperture throughout this work, we used one with a radius, R, of 100 pc, which corresponds to the 30Dor size estimates by Lopez et al. (2014) based on the Hα emission. A large number of the field clouds are located in Hα-bright regions (see Fig. 1). Similarly to the 30Dor clouds, such field clouds reside in active star-forming regions, and as a result can be affected by feedback as well. To address this point, we distinguish between three sets of the field clouds: all those outside the 30Dor aperture (hereafter full field clouds), only those field clouds that are located outside Hα-bright regions, and those that reside inside these regions (hereafter Hα-dim and Hα-bright clouds, respectively). In order to build the Hα-dim and Hα-bright samples, we located Hα-bright regions and excluded or selected the clouds whose centroid positions are found within these regions. We used the flux-calibrated continuum-subtracted Hα MCELS mosaic³ and applied the dilated masking technique with the upper and lower thresholds for the line integrated flux of 1.5 × 10⁻¹⁵ erg s⁻¹ cm⁻² and 45 × 10⁻¹⁵ erg s⁻¹ cm⁻², respectively. In addition, to exclude bright point sources, we introduced a size constraint and required an area of at least 1.6 × 10³ pc² for the Hα-bright regions above. The specific values of the constraints above were found by visually inspecting the resulting masks.

Within the R = 100 pc 30Dor aperture, full field, Hα-dim, and Hα-bright regions we identified 48, 2655, 1668, and 987 molecular clouds, respectively. Below we compare the 30Dor cloud sample with the clouds in the other three samples (full field, Hα-dim, and Hα-bright) as well as study how the difference between the 30Dor and field clouds changes with the size of the 30Dor aperture. In order to compare the samples, we considered their medians and applied the KS test with a confidence level of 95 %, rejecting the null hypothesis that the 30Dor and field samples are drawn from the same distribution if the p-value is less than 0.05.

3.1.1. Nearest neighbour separation and velocity

We find that, on average, the 30Dor clouds show smaller relative separations than the field clouds. In other words, compared with the field, the clouds in 30Dor are more closely spaced. To estimate relative separations between the clouds, we adopted the nearest neighbour separation, r_nnb, which we define as a distance between a specific cloud and its nearest neighbour in the PP plane. For this, we utilised the positions of the cloud centroids as derived from the dendrogram analysis. The distributions of r_nnb for the 30Dor and field clouds are shown in panel a of Fig. 2. We note that the 30Dor distribution peaks at smaller values than the field ones. The median value of r_nnb in 30Dor is 9.8 pc, which is approximately half that in the field, irrespective of whether the clouds from Hα-bright regions are included in the total field set (full field), excluded from it (Hα-dim), or solely make up this set (Hα-bright): 20.8 pc, 22.5 pc, and 18.3 pc, respectively. In all three cases, the KS test for the corresponding pairs of r_nnb samples gives p-values below the selected 0.05 threshold: 1.2 × 10⁻⁹ (30Dor vs. full field ), 5.9 × 10⁻¹¹ (30Dor vs. Hα-dim), and 2.2 × 10⁻⁸ (30Dor vs. Hα-bright), indicating with high confidence statistically significant differences in r_nnb between the 30Dor and field clouds.

Fig. 2. Comparison of the properties of the molecular clouds in 30Dor and the field. The panels show the distributions of the cloud properties, such as the nearest neighbour separation, rnnb (a), absolute value of the nearest neighbour velocity, |vnnb| (b), linewidth (c), virial mass, Mvir (d), cloud size (e), and CO line luminosity, $L_{\text{CO}}^{\prime }$ (f). The clouds were distinguished between 30Dor and the field based on a circular aperture with a radius of R = 100 pc, corresponding to the size of 30Dor. The aperture was centred on R136. The distributions of the 30Dor clouds are shown in coral (48 clouds). For the field, three cases are considered: all the clouds outside the aperture (2655 full field clouds; grey histograms), only those full field clouds that reside outside regions with bright Hα emission (1668 Hα-dim clouds; yellow-green histograms), and those that reside inside these regions (987 Hα-bright clouds; blue histograms). The medians of the distributions are shown by vertical lines. For rnnb and |vnnb|, the p-values for the KS tests comparing 30Dor with the field are below the selected 0.05 threshold, marking a significant difference between the corresponding samples. This is also the case for the linewidth when comparing the clouds of 30Dor with those of the full field and Hα-dim regions. We list all p-values in Table B.1.

Moreover, compared with the field, the 30Dor clouds move faster with respect to each other. To show this, we analysed the distribution of the absolute value of the nearest neighbour velocity, v_nnb, which we define as the difference between the line-of-sight peculiar velocity, v_pec, of a cloud and that of its nearest neighbour:

$$\begin{array}{c}\hfill \left|v_{\mathrm{nnb}}\right|=|v_{\mathrm{pec}};\mathrm{cloud}-v_{\mathrm{pec};\mathrm{nearest}\mathrm{neighbour}}|.\end{array}$$(1)

In turn, v_pec was derived from the centroid velocity, v_cen, which was obtained from the kinematic analysis and can be expressed as ⁴

$$\begin{array}{c}\hfill v_{\mathrm{cen}}=v_{\mathrm{pec}}+v_{\mathrm{rot}}+v_{\mathrm{sys}},\end{array}$$(2)

where v_rot is the rotational velocity of the disc of the LMC at the position of a cloud and v_sys is its systemic velocity. For the rotation of the disc, we followed Tsuge et al. (2019) and adopted the model from Corbelli & Schneider (1997):

$$\begin{array}{c}\hfill v_{\mathrm{rot}}\left(r\right)=v_{\infty }tanh(r/r_0),\end{array}$$(3)

where r is the galactocentric distance in the plane of the LMC disc, r₀ = 0.94 kpc is the radius where the velocity field changes from rigid to flat rotation, and v_∞ = 61.1 km s⁻¹ is the circular velocity at r > r₀. We assumed the coordinates of the galaxy centre RA = 05^h16^m24^s.82, Dec = −69°05′58″.88 and took into account the position angle of the major axis, p.a. = 200°, and corrected v_rot for the inclination of the galaxy, i = 35°. The values of the coordinates and parameters above were taken from the fits by Tsuge et al. (2019). The value of the systemic velocity, v_sys, is not important for our analysis since it cancels out when calculating v_nnb. The distributions of the absolute value of v_nnb are given in panel b of Fig. 2. The 30Dor distribution shows several peaks that are all found at higher values than those of the peaks of the field distributions. The median |v_nnb| in the 30Dor sample is 10.4 km s⁻¹, which is approximately thrice larger than for the field clouds: 3.5 km s⁻¹ (full field), 3.3 km s⁻¹ (Hα-dim), and 4.1 km s⁻¹ (Hα-bright). In all three cases, the KS test gives p-values that allow us to conclude that the 30Dor and field samples are highly different in terms of |v_nnb|: 1.1 × 10⁻⁸ (30Dor vs. full field), 3.9 × 10⁻¹⁰ (30Dor vs. Hα-dim), and 3.1 × 10⁻⁶ (30Dor vs. Hα-bright).

3.1.2. Linewidth

To estimate the linewidth of the cloud spectra, we adopted the velocity dispersion, σ_v, from the dendrogram analysis and converted it to a linewidth (FWHM) using $FWHM=2\sqrt{2ln2}{\sigma }_v$. The distributions of the cloud linewidths are shown in panel c of Fig. 2. In contrast to the field distributions, that of 30Dor has several peaks, with the left-most peak (corresponding to the smallest values among the three peaks) coincident with the peaks of the field distributions. The other two 30Dor peaks indicate a presence of clouds with enhanced line profiles in 30Dor as compared with the field, which is also seen in the medians: 2.69 km s⁻¹ (30Dor), 2.02 km s⁻¹ (full field), 1.92 km s⁻¹ (Hα-dim), and 2.16 km s⁻¹ (Hα-bright). Based on the KS test, there is a statistically significant difference between the linewidths of the 30Dor clouds and those residing in non-active star-forming regions: Hα-dim (p-value ≈ 2.9 × 10⁻³) and the full field, which is also dominated by Hα-dim clouds (p-value ≈ 1.2 × 10⁻²). However, this is not the case when comparing linewidths between 30Dor and active star-forming regions (i.e. Hα-bright): the p-value is above the selected threshold (0.1 > 0.05), so although the corresponding samples can still be statistically different, we cannot confirm it based on the KS test with a 95 % confidence.

3.1.3. Size, mass, and CO line luminosity

As opposed to r_nnb, |v_nnb|, and the linewidth, the rest of the cloud properties analysed do not show a statistically significant difference between 30Dor and the field. These properties are the size, mass, and CO line luminosity, $L_{\text{CO}}^{\prime }$, of the clouds. As a size estimate, we employed the area of the PP projections of the clouds derived from the dendrogram analysis. For the mass, we used the virial mass, M_vir, which was obtained from a virial analysis. To derive $L_{\text{CO}}^{\prime }$, we integrated within the cloud contours in the PPV space. More details on calculating the size, M_vir, and $L_{\text{CO}}^{\prime }$ are given in Appendix B. The corresponding distributions are shown in panels d–f of Fig. 2. In all cases, the p-values for the KS test comparing the properties above between the 30Dor and field clouds are larger than the threshold of 0.05. It is worth noting that the virial mass is covariant with the CO line luminosity and size, which is seen in the shape of the corresponding distributions.

3.1.4. Summary of the cloud properties

We summarise the results obtained for the individual cloud properties above in two tables in the Appendix: Table B.1 (medians) and Table B.2 (p-values). We note that for the dimmest and smallest structures, our cloud catalogue is incomplete. We estimated the completeness by simulating clouds in the emission-free channels of our data cube followed by measuring the detection rate of the corresponding structures. From these simulations, we find that our data are 80 % complete at a line luminosity of $L_{\text{CO}}^{\prime }$ ∼ 55 K km s⁻¹ pc² at a 3 km s⁻¹ linewidth⁵. Having repeated the analysis in the subsections above for the clouds with $L_{\text{CO}}^{\prime }$ > 55 K km s⁻¹ pc² only, we found that incompleteness of faint clouds does not introduce significant differences in our results. For this reason, we use the full cloud catalogue for our analysis throughout the paper but also give the results obtained using a cut at the 80 % completeness in the Appendix (see Tables B.3 and B.4). We speculate that, due to the covariance of $L_{\text{CO}}^{\prime }$, linewidth, and the size and similarity of the $L_{\text{CO}}^{\prime }$ distributions in 30Dor and the field, selection effects related to the linewidth should equally affect the distributions of the cloud properties in the 30Dor and field regions.

3.2. Radial trends

Above, we distinguished between the 30Dor and field clouds based on the R = 100 pc circular aperture corresponding to the size of 30Dor. In this subsection, to study how the cloud properties evolve with the distance to the centre of 30Dor, we consider a grid of circular apertures around the centre of 30Dor with radii ranging from 50 pc to 600 pc with a step of 10 pc. The lower limit was selected so that the corresponding 30Dor sample still contains 12 clouds for the sake of statistical significance, whereas the upper one was chosen such that the outermost aperture still has an overlap of 95 % with the analysed region. For every aperture of the grid, we replicated the approach from Sect. 3.1; namely, we built two sets of clouds, the inner and the outer ones, that is all the clouds that are located inside and outside the corresponding aperture⁶, respectively, and compared medians of the cloud properties of these two sets. Specifically, we calculated the medians of the nearest neighbour separation, r_nnb, and velocity, |v_nnb|, CO line luminosity, $L_{\text{CO}}^{\prime }$, virial mass, M_vir, size, and linewidth for the inner clouds and normalised them by the corresponding medians calculated for the outer clouds. In addition, we introduced the areal number density, Σ_N, which we define as the number of the inner clouds divided by the observed area within the corresponding aperture. As an evolution marker, we used the ratio of Σ_N to its mean value within the full analysed coverage – the total number of clouds (2703) divided by the total observed area (10.5 kpc²). We find a total of 2284 clouds to be located outside the outermost aperture considered, which constitutes ∼85 % of the full cloud sample analysed; thus, given the coverage, the outer clouds well represent the overall population of molecular clouds in the LMC at all the aperture radii considered. Figure 3 shows the inside-to-outside medians ratios and the Σ_N-ratio as functions of the aperture radius. We calculated uncertainties of the ratios by using Poisson noise for Σ_N and uncertainties of the medians for the rest of the properties. In turn, uncertainties of the medians were derived from those of the means using the formula ${\mathrm{\Delta }}_{\mathrm{median}}={\mathrm{\Delta }}_{\mathrm{mean}}\sqrt{\pi (2n+1)/4n}$, where 2n + 1 is the sample size (Kenney & Keeping 1962, pp. 211-212). More details on the uncertainties are given in Appendix C.

Fig. 3. Evolution of the cloud properties. The figure shows the inside-to-outside medians ratios for |vnnb|, rnnb, linewidth, |Mvir|, size, and $L_{\text{CO}}^{\prime }$ as well as the ΣN ratio as a function of the aperture radius centred on R136 (see text). The y-axis is shown using a logarithmic scale.

From Fig. 3, Σ_N, |v_nnb|, and r_nnb vary significantly with the aperture radius. Looking into details, Σ_N is the largest in the inner apertures and steeply decreases outwards from the centre of 30Dor. |v_nnb| shows larger values compared with the rest of the LMC disc up to a radius of ∼230 pc, with a pronounced peak at ∼80 pc. There are monotonic trends for r_nnb (increasing) and the linewidth (decreasing) with increasing aperture radius. M_vir shows seemingly high values in the inner apertures up to a radius of ∼260 pc; however, contrary to the properties discussed above, the uncertainty for the M_vir medians ratio is large, which does not allow us to conclude that 30Dor contains higher-mass clouds compared with the rest of the LMC disc. Moreover, these high median values of M_vir may arise due to a bias, since there is only one massive cloud found in the central part of 30Dor. We discuss this further in Sect. 4 and show that the presence of such a cloud in 30Dor is not unlikely from a statistical point of view given the cloud population of the galaxy. $L_{\text{CO}}^{\prime }$ and the cloud size do not show significant deviations from their medians calculated for the rest of the LMC disc. We note that the uncertainties for $L_{\text{CO}}^{\prime }$ and the size are relatively large (∼50 % and ∼20 % on average, respectively) and, since these properties do not demonstrate explicit peculiarities, we do not show their uncertainties in Fig. 3, for ease of reading the plot.

One would expect the inside-to-outside medians as well as the Σ_N- ratios to scale to one at large radii from the centre of 30Dor. For several of the cloud properties (Σ_N, |v_nnb|, r_nnb), this is not the case within the aperture radii grid considered. However, there are active star-forming regions located to the south of 30Dor falling within this range of radii (as can be seen in Hα emission; see Fig. 1). Like in 30Dor, clouds in such regions should be affected by feedback, and thus it is not surprising that Σ_N and |v_nnb| show larger values and r_nnb shows lower ones in this part of the LMC disc than in the galaxy overall.

3.3. Maps of the cloud properties

So far we have compared the 30Dor clouds with all the field clouds. However, this approach deals with samples of significantly different sizes. Moreover, the field clouds are distributed all over the LMC disc and their properties may depend on the environment. In this subsection, we build maps of the cloud properties to study their variations within the analysed coverage and check if 30Dor stands out. To build the maps, we set a 2D grid with a pixel size of ∼2.5 pc, corresponding to half the beam size. To avoid low number statistics effects, we considered only those pixels of the grid that contain at least five clouds within a radius of 100 pc (corresponding to the size of 30Dor; see above). We used the full catalogue of the cloud properties and iterated over the selected pixels of the grid, calculating distance-weighted medians for r_nnb, |v_nnb|, $L_{\text{CO}}^{\prime }$, M_vir, size, and linewidth so that the clouds that were closer to the pixel considered had larger weights and contributions to the medians than the clouds that were further out. The weights were assigned to the clouds according to a window function, for which we adopted a 2D Gaussian with a FWHM of 200 pc corresponding to the diameter of 30Dor. Using a window function allowed us to derive smooth distributions of the cloud properties. For every pixel of the grid, the weights were calculated as the values of the Gaussian at the positions of the clouds relative to the pixel considered. In addition to the cloud properties mentioned above, we considered a sum of the window weights, Σ_iw_i, as a proxy for the distance-weighted areal number density, Σ_N. If the Gaussian window peaks at a crowded region with a large number of clouds per unit area (i.e. high Σ_N), then Σ_iw_i will be large as well and vice versa. The maps for Σ_iw_i, r_nnb, and |v_nnb| are shown in panels a–c of Fig. 4. In the Appendix, we show the corresponding distributions for all the properties analysed including $L_{\text{CO}}^{\prime }$, M_vir, size, and linewidth.

Fig. 4. Maps of the clouds properties (see Sect. 3.3). The panels show a sum of the window function weights, Σiwi (a), weighted medians for the nearest neighbour separation, rnnb (b), and the absolute value of the nearest neighbour velocity, vnnb (c), and the probability of finding clouds with properties more extreme than those in a given point (d). The corresponding zooms into 30Dor are shown in the upper right corners of each individual map. The majority of low-probability “spots” in panel d are visually associated with known active star formation regions in the LMC (N158, N159, etc.), except for one spot marked by the yellow box.

As seen in Fig. 4, the largest values of Σ_iw_i are found in 30Dor. It also hosts one of the troughs for r_nnb. Regarding |v_nnb|, there is a peak found to the southeast of 30Dor that overlaps with the 100 pc aperture around it. This peak contributes to the high |v_nnb| values found in 30Dor, as shown in Sect. 3.1.1, and its effect can also be seen in Fig. 3, where the |v_nnb| bump extends up to a radius of ∼230 pc from the centre of 30Dor.

Figure 4 clearly shows that the population of 30Dor clouds is unique in terms of such properties as the areal number density, nearest neighbour separation, and velocity in the LMC. In order to demonstrate this quantitatively, based on the property maps, we built probability maps. We estimated the probability of finding (i) larger Σ_iw_i, (ii) lower r_nnb, and (iii) higher |v_nnb| compared to the respective values at a given point. We focused on Σ_iw_i, r_nnb, and |v_nnb| only since the rest of the properties ($L_{\text{CO}}^{\prime }$, M_vir, size, and linewidth) do not show a pronounced difference between 30Dor and the field (see Fig. B.1). The probabilities were calculated by integrating the corresponding normalised distributions. Finally, we calculated the probability of finding cloud properties more extreme than the ones at a given point as a product of the three individual probabilities above. The resulting probability map is shown in panel d of Fig. 4. From the map, the lowest probabilities in the entire region analysed are found in 30Dor, with the mean and median values inside the 100 pc radius of 30Dor being 2.4 × 10⁻⁵ and 1.8 × 10⁻⁶, respectively.

We note that 30Dor is not the only region showing peculiar cloud properties, which is clearly seen in the probability map. Of particular interest is the region in the western part of the LMC disc that shows notably low probabilities. The region is marked by the yellow box in panel d of Fig. 4. This low-probability spot is a result of high nearest neighbour velocities detected in that place (see panel c of Fig. 4). A moment-one map (the velocity distribution; see panel b of Fig. 5) shows that there are two groups of clouds at this location, which move with the line-of-sight velocities separated by ∼20 km s⁻¹.

Fig. 5. CO and HI moment-one maps with a modelled rotation of the LMC disc subtracted (see Sect. 3.1.1 for the used rotation model and its parameters). The panels show zooms into (a) the molecular ridge and CO Arm of the LMC and (b) the low-probability region marked by the yellow box in panel d of Fig. 4. The pink contours show the high |vnnb| regions (see panel c of Fig. 4) corresponding to (a) 9 km s−1 (outer) and 10 km s−1 (inner) and (b) 10 km s−1. The solid black lines outline the analysed survey coverage.

4. Discussion

30 Doradus is a complex and diverse region in terms of stellar and gas populations. Schneider et al. (2018) find that the stellar population in the 30Dor features various ages, with R136 having formed last with stars still being formed in its vicinity. According to Schneider et al. (2018), there are two concentrations of young OB stars in the region – in the rich open cluster NGC 2070 and in the vicinity of the supernova remnant N157B (Henize 1956; Long & Helfand 1979; Danziger et al. 1981) – yet OB stars are relatively scattered, mainly in the western part of the region within a 100 pc radius. In R136, which is in the centre of NGC 2070, a number of Wolf-Rayet and O-type stars with masses exceeding 100 M_⊙ are found (Crowther et al. 2010, 2016; Bestenlehner et al. 2020; Kalari et al. 2022). Thus, there are many young massive stars in 30Dor that may drive a pre-SN feedback that plays a crucial role in dispersing molecular gas (e.g. Kim et al. 2021, 2022; Chevance et al. 2023; Ward et al. 2022). N157B, at the edge of 30Dor, does not play a significant role in the region in the context of feedback, as found by Lopez et al. (2011), nor does it affect the surrounding molecular gas (Micelotta et al. 2009). On the other hand, 30Dor contains a number of X-ray bubbles filled with hot plasma (Townsley et al. 2006) that shape the dynamics and large-scale structure of 30Dor (Pellegrini et al. 2011). Our data do not allow us to distinguish between different feedback mechanisms, locate specific feedback sources, or measure the exact scales from these sources at which feedback operates in the region. However, our analysis suggests that, overall, feedback affects the cloud population in 30Dor out to a radius of ∼180 pc from its centre, which follows from the probability map shown in panel d of Fig. 4 and corresponds to a 4σ cut of a Gaussian fit for the underlying probability distribution.

The higher number density, Σ_N, and the lower nearest neighbour separation, r_nnb, in 30Dor as compared with the field can be interpreted in the context of cloud fragmentation and dispersion caused by feedback. In this scenario, feedback has already dispersed one or a group of giant molecular clouds in 30Dor into a number of smaller-sized cloudlets. This would naturally explain the observed high number density and small nearest neighbour separations of the clouds in 30Dor. This interpretation is also supported by the fact that Σ_N shows several peaks in Fig. 4 that are associated with other star formation regions in the galaxy; specifically, N158, 159, and 160 to the south of 30Dor, and N44 and N79 in the central and western regions of the LMC disc, respectively. The reason why not all the star formation regions in the LMC show peaks and troughs of Σ_N and r_nnb similar to the ones of 30Dor may be related to the evolutionary sequence of molecular clouds, meaning that at early stages of star formation a giant molecular cloud may have not yet been dispersed, leading to a low Σ_N, whereas at late stages of star formation most of the gas in clouds may have been consumed or dispersed already, increasing relative separations between clouds and lowering the overall number of cloudlets.

Regarding the large values of |v_nnb| in 30Dor, these can be explained as the clouds being pushed away from a feedback source, an effect of large-scale gas motions, in particular, the known HI flows colliding towards the HI Ridge (Fukui et al. 2017), or a combination of both. In panel a of Fig. 5, we show CO and HI moment-one maps of the region that contains the molecular ridge (and 30Dor north of it) and the CO Arc (Mizuno et al. 2001) of the LMC to illustrate the cloud as well as large-scale gas kinematics in this part of the LMC disc. In these moment-one maps, we removed the LMC disc rotation using the same model and parameters as in Sect. 3.1.1. The HI moment-one map was derived using the combined ATCA and Parkes HI archival data of Kim et al. (1998, 2003). In the CO moment-one map, it is seen that the cloud velocity field in the region to the southeast of R136 is scattered, which results in high |v_nnb| values for the clouds residing there. In turn, the HI moment-one map shows that there may be flows of gas colliding in the region where the high |v_nnb| are detected. However, we note that the HI moment-one map may be insufficient in the case of a complex velocity field and that, in order to accurately study the large-scale kinematics of the gas in the LMC disc based on HI, one needs to distinguish between different velocity components (see e.g. Luks & Rohlfs 1992; Fukui et al. 2017; Tsuge et al. 2019; Oh et al. 2022). Moreover, a detailed study of possibly colliding flows of gas in the above-mentioned high |v_nnb| region would ideally require HI data with an angular resolution higher than the 1′ of the combined ATCA+Parkes data sets.

The clouds of 30Dor are known to feature larger linewidths at a given size compared to the Milky Way (e.g. Indebetouw et al. 2013; Nayak et al. 2016; Kalari et al. 2018). Moreover, Wong et al. (2017, 2019) find this also to be the case as compared with clouds from different regions in the LMC (six cloud complexes and 30Dor considered in total in these two works). From Wong et al. (2019), it also follows that, at a given size, linewidth shows a positive correlation with star formation activity (see the upper left panel in their Fig. 8). Our analysis also suggests signatures of this effect, which is seen in the shape of the linewidth distributions (see panel c of Fig. 2) and their medians: 2.69 km s⁻¹, 2.16 km s⁻¹, and 1.92 km s⁻¹ for 30Dor, Hα-bright, and Hα-dim clouds, respectively. At the same time, in our analysis, the linewidth difference between the 30Dor and non-30Dor clouds is not as pronounced as in the case of |v_nnb|, |r_nnb|, and Σ_N, especially when considering the cloud property maps (see Sect. 3.3). In addition, this linewidth difference is not as evident as in the studies mentioned above, which, however, is not necessarily surprising because their higher spatial resolution (using ALMA) allowed them to resolve the substructure of the clouds in the inner parts of 30Dor, providing better statistics for the broadened line profiles.

There is no statistically significant difference found between 30Dor and the field when comparing the rest of the properties: the CO line luminosity, $L_{\text{CO}}^{\prime }$, virial mass, M_vir, and size. Interestingly, Jeffreson et al. (2021) in their HII region feedback simulations find clouds in the mass range of 4.8 ≲ log(M/M_⊙) ≲ 5.7 to live longer due to feedback, as opposed to the less massive clouds with masses log(M/M_⊙) ≲ 4.8, whose lifetimes were reduced. In apparent contradiction to this is our M_vir distribution for 30Dor: the high mass range of log(M_vir/M_⊙) ≳ 4.7 contains just one cloud (see panel d of Fig. 2). However, such a deficit is expected, since clouds in this high-mass regime are rare in the LMC: according to our cloud catalogue, the number density for the clouds with log(M_vir/M_⊙) ≳ 4.7 is ∼17 kpc⁻², which translates to ∼0.5 clouds to be expected in a 100 pc aperture. In addition, given that our analysis is based on a relative comparison between the clouds, we used the virial mass as a legitimate mass estimate but it may not be the best representation of the actual distribution of the cloud masses since it relies on the assumption of clouds being virialised, which is not guaranteed. In turn, calculation of the actual physical mass and density of molecular clouds from observations is a challenging task that is beyond the scope of this work and will be considered in forthcoming papers.

The 30Dor Nebula is not the only active star-forming region in the LMC. However, in terms of activity, 30Dor is certainly unique, with a ∼15 % contribution to the total Hα-flux of the LMC as measured using the flux calibrated continuum subtracted Hα MCELS mosaic. Another signature of the 30Dor uniqueness is the properties of its clouds: in terms of Σ_N, r_nnb, and |v_nnb|, 30Dor stands out even when compared with the other active star-forming regions in the LMC. At the same time, we find a number of regions in the LMC whose molecular clouds show extreme (although not as extreme as in 30Dor) properties (see Fig. 4). The majority of such regions are at least visually associated with known star formation regions in the LMC (see e.g. Lawton et al. 2010; Lopez et al. 2014; Okada et al. 2019), which are marked in panel d of Fig. 4. We particularly note that there is a region with high nearest neighbour velocities that is not associated with any of the known active star formation regions and that does not show obvious signs of it in Hα (see Fig. 1). We mark this region with a yellow box in panel d of Fig. 4. Based on the CO moment-one map shown in panel b of Fig. 5, we speculate that the extreme |v_nnb| values found in that region are likely to be the result of a random superposition of two groups of clouds with different velocities, whereas the clouds may belong to two not necessarily colliding gas flows. This region is of significant interest but studying it in detail is beyond the scope of this paper.

There is a number of works showing that CO may not be a reliable tracer of H₂ in feedback-affected regions (e.g. Chevance et al. 2020c; Ebagezio et al. 2023). This is because the CO molecule can be destroyed by strong radiation fields, particularly at low metallicity. If this is the case for 30Dor and the actual distribution of H₂ is less spatially clustered compared with what is traced by CO, then feedback (i) indeed reaches the scales at which this CO clustering is observed, but (ii) does not disperse the clouds as much as is seen in the CO line. As a result, our estimates of number density and size in 30Dor can be biased towards the higher and lower values, respectively, while the separation between the clouds can be either under- or overestimated. Nevertheless, the higher velocities would then trace internal motions of gas “cloudlets” inside larger clouds, which is by itself a signature of gas dispersion, displacement, or a combination of both. As regards using other tracers along with CO, they are either not available for relatively extended areas in the LMC disc or, like dust data (e.g. Herschel-Heritage; Meixner et al. 2013), follow the CO distribution in it.

5. Conclusions

We have compared the properties of the molecular clouds of 30Dor with those of the clouds in the rest of the LMC disc. Our study is based on the new observational data in the ¹²CO(3 − 2) line obtained in the framework of the currently ongoing APEX LMC Legacy Survey (PI: Axel Weiss). The data have a spatial resolution of ∼5 pc and cover ∼13.8 sq. deg. (10.5 kpc²) of the LMC disc.

We have analysed such properties of the clouds in the LMC as: the separation from the nearest neighbour in the PP plane, r_nnb; the absolute value of the velocity relative to the nearest neighbour cloud, |v_nnb|; the CO line luminosity, $L_{\text{CO}}^{\prime }$; the PP plane projection area; the virial mass, M_vir; and the linewidth of cloud spectrum. In addition, we have estimated the areal number density of clouds, Σ_N, in the LMC disc.

We find that the clouds of 30Dor differ from those in the rest of the LMC disc in several cloud properties from the list above. The properties showing the most significant difference are the nearest neighbour separation, r_nnb, the absolute value of the nearest neighbour velocity, |v_nnb|, and the areal number density, Σ_N. Compared with the rest of the LMC, the clouds of 30Dor have a smaller r_nnb and a higher |v_nnb|. These differences in r_nnb and |v_nnb| between 30Dor and the rest of the LMC disc are statistically significant, as confirmed by the KS test with a confidence level of 95 %. Regarding Σ_N, it is 30Dor where the highest values of Σ_N are found within the analysed coverage.

We note that the linewidth distribution of clouds in 30Dor shows secondary peaks at large values contrary to that of the rest of the clouds within the analysed coverage. On average, the linewidth in 30Dor is larger than outside of it. However, this distinction between the clouds of 30Dor and those in the rest of the LMC disc is not as pronounced as for r_nnb, |v_nnb|, and Σ_N, although the KS test still gives p-values below the 0.05 threshold (95 % confidence), except when comparing the clouds of 30Dor with those residing exclusively in Hα-bright regions.

At the same time, we find no statistically significant differences in $L_{\text{CO}}^{\prime }$, projected area, and M_vir at the observed scales between the clouds of 30Dor and those in the rest of the LMC disc.

We interpret the findings above as signatures of cloud fragmentation and dispersion due to feedback. These signatures are detected up to scales as large as ∼180 pc from the centre of 30Dor. Among the parameters identified as potential feedback tracers, the areal cloud number density, Σ_N, appears to be enhanced not only in 30Dor but in the other known star formation regions of the LMC as well. This supports the idea that Σ_N is linked to the evolutionary state of the molecular gas. We caution, however, that line-of-sight projection effects as well as kinematically complex motions (e.g. large-scale gas flows) may mimic the signatures of feedback identified in 30Dor in other regions of the galaxy.

Acknowledgments

K.G. is a member of the International Max Planck Research School (IMPRS) for Astronomy and Astrophysics at the Universities of Bonn and Cologne. M.C. gratefully acknowledges funding from the DFG through an Emmy Noether Research Group (grant number CH2137/1-1). COOL Research DAO is a Decentralised Autonomous Organization supporting research in astrophysics aimed at uncovering our cosmic origins. R.H.-C. thanks the Max Planck Society for support under the Partner Group project “The Baryon Cycle in Galaxies” between the Max Planck for Extraterrestrial Physics and the Universidad de Concepción. R.H-C. also gratefully acknowledges financial support from Millenium Nucleus NCN19058 (TITANs), and ANID BASAL projects ACE210002 and FB210003. C.-H.R.C. acknowledges support from the Deutsches Zentrum für Luft- und Raumfahrt (DLR) grant NS1 under contract no. 50 OR 2214. We thank the APEX staff and astronomers involved in observing the LMC both remotely and on-site for this project and the ones yet to come. We thank the anonymous referee for the valuable and constructive comments.

References

Appendix A: Masking of the data

In order to distinguish between real molecular clouds and noise, we set a number of constraints. Namely, a potential cloud must have

1. a high signal-to-noise ratio (S/N);

2. reasonably wide line profiles, as opposed to noisy spikes;

3. a size larger than the beam size, in order to be resolved.

To satisfy these constraints, we used a masking algorithm based on the dilated masking approach (Rosolowsky & Leroy 2006; Rosolowsky et al. 2021). This approach allowed us to retain not only the brightest regions of clouds but also their dimmer outskirts in the PPV space.

As a first step, we built a noise map, σ_N, by stacking a number of emission-free channels of the data cube and then calculating the rms noise along the velocity axis. Having set three S/N thresholds, {R₁, R₂, R₃}, such that R₁ < R₂ and R₁ < R₃, we found confined regions⁷ in the data cube where T ≥ R₁σ_N and retained only those that contained (i) at least one voxel with T ≥ R₂σ_N and (ii) at least two voxels in a row along the velocity axis for which T ≥ R₃σ_N. The voxels in (i) and (ii) were not required to overlap. After that, we excluded those of the confined regions left, which were smaller than the beam size in both the x and y directions. Finally, we checked the number of possible “false detections” that could have passed the checks above to adjust the {R₁, R₂, R₃} constraints. We did so by running the algorithm for the data cube multiplied by a factor of −1. In this study, we fixed R₁ = 2 and R₂ = 4.5 and iterated over a grid of R₃ values, aiming to reach a rate of false detections below 0.5 percent. We found that, for our specific data cube, R₃ = 3.5 corresponds to 6/2703 ∼ 0.2 percent false/real detections, and adopted it.

Fig. A.1. RMS noise map. In the plot, the high-value tail of the distribution is cut at ∼0.23 K; the RMS noise values above this cut are mainly found in the regions on the outskirts of the coverage that were not considered in this work.

Appendix B: Properties of the clouds

We used the astronomical dendrograms to locate the clouds in our observational data and estimate some of their properties, following Solomon et al. (1987), Rosolowsky & Leroy (2006), Colombo et al. (2019). In addition to the constraints listed in Appendix A, we introduced another requirement: the area of the PP projection of a cloud must be larger than the squared beam size. This was done to exclude diagonal stripe-like structures that could have passed the beam check above.

We adopted the mean value of the rms noise map, MEAN(σ_N), as the main beam surface brightness sensitivity and used it when building the dendrogram tree setting, MIN_DELTA = 2 × MEAN(σ_N) ≈ 0.3 K. The trunk of the resulting dendrogram was used as a representation of the observed cloud population. Below we list the formulae we used to estimate the properties of molecular clouds.

Fig. B.1. Distributions of the molecular clouds’ properties (see Sect. 3.3). The panels show: the distributions of a sum of the window function weights, Σiwi, which we used as a proxy for the weighted areal number density (a); the separation between the nearest neighbour clouds, rnnb (b); the absolute value of the nearest neighbour velocity, |vnnb| (c); the virial mass, Mvir (d); size (e); linewidth (f); CO line luminosity, $L_{\text{CO}}^{\prime }$ (g); and the probability (h; see panel d of Fig. 4). The red and grey histograms show the distributions of the cloud properties for the data points inside and outside the 30Dor aperture, respectively. The 30Dor aperture was centred on R136, assuming a radius of 100 pc. The vertical blue lines show the medians of the distributions: 30Dor (dashed) and field (i.e. outside 30Dor; solid). The y axes are shown using a logarithmic scale. We note that the Σiwi, rnnb, |vnnb| distributions show a clear difference between the 30Dor region and the rest of the observed field as opposed to all the other properties analysed.

One of the parameters derived from the dendrogram analysis is the area of the projection of a cloud onto the PP plane, A_PP. This was derived by counting the number of pixels enclosed within the contours of the PP projection of a cloud, which we then converted to pc² using the pixel size. We assumed that clouds have 2D Gaussian profiles and, following this assumption, A_PP should correspond to the area under a Gaussian, which allowed us to estimate the effective radius of a cloud, R_eff, as

$$\begin{array}{c}\hfill R_{\mathrm{eff}}\approx \frac{1}{2}\sqrt{\frac{A_{\mathrm{PP}}}{1.133}}.\end{array}$$(B.1)

We corrected R_eff for the convolution with a Gaussian beam:

$$\begin{array}{c}\hfill R_{\mathrm{eff}}^{\mathrm{corr}}=\sqrt{R_{\mathrm{eff}}^2-R_{\mathrm{beam}}^2},\end{array}$$(B.2)

where R_beam is a half-width at the half maximum of the beam (≈2.5 pc). Once R_eff had been deconvolved, we corrected A_PP:

$$\begin{array}{c}\hfill A_{\mathrm{PP}}^{\mathrm{corr}}=1.133{(2R_{\mathrm{eff}}^{\mathrm{corr}})}^2.\end{array}$$(B.3)

This corrected PP projection area, $A_{\text{PP}}^{\text{corr}}$, which we refer to as the “cloud size,” was used throughout the work.

Regarding the cloud mass, for the sake of simplicity, we assumed that molecular clouds are virialised and, following Bertoldi & McKee (1992), we estimated their virial masses using the formula

$$\begin{array}{c}\hfill M_{\mathrm{vir}}=\frac{5}{G}{\sigma }_v^2{R}_{\mathrm{eff}}^{\mathrm{corr}},\end{array}$$(B.4)

where G = 1/232 pc $M_{\odot }^{-1}$ km² s⁻² is the gravitational constant and σ_v is the velocity dispersion, which was derived from the dendrogram analysis.

The CO line luminosity of a cloud was measured as

$$\begin{array}{c}\hfill L_{\mathrm{CO}}^{{}^{\prime }}\left[{\mathrm{K}\mathrm{km}\mathrm{s}}^{-1}{\mathrm{pc}}^2\right]={\mathrm{\Sigma }}_i{T}_i\mathrm{\Delta }v{(D_{\mathrm{LMC}}tan\left(\mathrm{\Delta }s\frac{\pi }{180}\right))}^2,\end{array}$$(B.5)

where T_i [K] is the main beam brightness temperature and the summation is taken over all the voxels within the PPV-structure corresponding to a specific cloud; D_LMC = 50 kpc is the distance to the LMC, Δs is the size of a pixel, and Δ_v is a spectral resolution of the data cube in units of deg and km s⁻¹, respectively.

Appendix C: Estimation of uncertainties

In order to estimate uncertainties of the cloud properties, we began by setting individual uncertainties of the parameters that we measured in our data cube. Namely, we adopted the sensitivity limit of 0.154 K as an uncertainty for the brightness temperature. Half the beam size was used as an uncertainty for the cloud position in the PP plane, that is for the centroid x and y coordinates of a cloud. A spectral resolution of the data cube, Δ_v = 0.5 km s⁻¹, was adopted as an uncertainty for the coordinate of a cloud along the velocity axis, that is the centroid velocity, v_cen (see Sect. 3.1.1). Then, given that a cloud property is given by an expression f = f(x₁, x₂, ...), its uncertainty was calculated using a basic formula

$$\begin{array}{c}\hfill \mathrm{\Delta }f=\sqrt{{\left(\frac{\partial f}{\partial x_1}\mathrm{\Delta }{x}_1\right)}^2+{\left(\frac{\partial f}{\partial x_2}\mathrm{\Delta }{x}_2\right)}^2+...}.\end{array}$$(C.1)

We used the approach above for all the cloud properties (including the ratios in Sect. 3.2) except the areal number density, Σ_N. However, for the cloud projection area, A_PP, and CO line luminosity, $L_{\text{CO}}^{\prime }$, we extended the method. Namely, we raised the lowest signal-to-noise constraint from 2σ_N to 3σ_N (see Appendix A), calculated the corresponding values (with uncertainties) of the parameters, and then measured their uncertainties as

$$\begin{array}{cc}\hfill \mathrm{\Delta }{A}_{\mathrm{PP}}& =A_{\mathrm{PP}}(2{\sigma }_N)-A_{\mathrm{PP}}(3{\sigma }_N)\hfill \end{array}$$(C.2)

$$\begin{array}{cc}\hfill \mathrm{\Delta }{L}_{\mathrm{CO}}^{{}^{\prime }}& =L_{\mathrm{CO}}^{{}^{\prime }}(2{\sigma }_N)-L_{\mathrm{CO}}^{{}^{\prime }}(3{\sigma }_N).\hfill \end{array}$$(C.3)

For an uncertainty of the areal number density, Σ_N, we used Poisson noise: $\mathrm{\Delta }{\mathrm{\Sigma }}_{\mathrm{N}}=\sqrt{N}/A_{\mathrm{obs}}$, where N is the number of clouds and A_obs is the observed area within which clouds are counted.

All Tables

All Figures

Fig. 1. Large Magellanic Cloud. The underlying image is the Hα MCELS map (Smith & MCELS Team 1998). Panel a shows the coverage of the ongoing APEX CO line Legacy Survey (white line) analysed in this work and (in yellow-green) the contours of the CO moment-zero map, with the level corresponding to the signal-to-noise threshold of S/N = 2 in the masked CO data. The moment-zero contours overlapping with Hα-bright regions (see Sect. 3) are highlighted in blue. The 30Dor region is marked by the pink circle with a radius of 100 pc that is centred on the position of R136. Panel b is a zoom into the 30Dor region. The positions of the molecular clouds are shown by X marks.

In the text

Fig. 2. Comparison of the properties of the molecular clouds in 30Dor and the field. The panels show the distributions of the cloud properties, such as the nearest neighbour separation, rnnb (a), absolute value of the nearest neighbour velocity, |vnnb| (b), linewidth (c), virial mass, Mvir (d), cloud size (e), and CO line luminosity, $L_{\text{CO}}^{\prime }$ (f). The clouds were distinguished between 30Dor and the field based on a circular aperture with a radius of R = 100 pc, corresponding to the size of 30Dor. The aperture was centred on R136. The distributions of the 30Dor clouds are shown in coral (48 clouds). For the field, three cases are considered: all the clouds outside the aperture (2655 full field clouds; grey histograms), only those full field clouds that reside outside regions with bright Hα emission (1668 Hα-dim clouds; yellow-green histograms), and those that reside inside these regions (987 Hα-bright clouds; blue histograms). The medians of the distributions are shown by vertical lines. For rnnb and |vnnb|, the p-values for the KS tests comparing 30Dor with the field are below the selected 0.05 threshold, marking a significant difference between the corresponding samples. This is also the case for the linewidth when comparing the clouds of 30Dor with those of the full field and Hα-dim regions. We list all p-values in Table B.1.

In the text

	Fig. 3. Evolution of the cloud properties. The figure shows the inside-to-outside medians ratios for |vnnb|, rnnb, linewidth, |Mvir|, size, and $L_{\text{CO}}^{\prime }$ as well as the ΣN ratio as a function of the aperture radius centred on R136 (see text). The y-axis is shown using a logarithmic scale.
In the text

Fig. 4. Maps of the clouds properties (see Sect. 3.3). The panels show a sum of the window function weights, Σiwi (a), weighted medians for the nearest neighbour separation, rnnb (b), and the absolute value of the nearest neighbour velocity, vnnb (c), and the probability of finding clouds with properties more extreme than those in a given point (d). The corresponding zooms into 30Dor are shown in the upper right corners of each individual map. The majority of low-probability “spots” in panel d are visually associated with known active star formation regions in the LMC (N158, N159, etc.), except for one spot marked by the yellow box.

In the text

Fig. 5. CO and HI moment-one maps with a modelled rotation of the LMC disc subtracted (see Sect. 3.1.1 for the used rotation model and its parameters). The panels show zooms into (a) the molecular ridge and CO Arm of the LMC and (b) the low-probability region marked by the yellow box in panel d of Fig. 4. The pink contours show the high |vnnb| regions (see panel c of Fig. 4) corresponding to (a) 9 km s−1 (outer) and 10 km s−1 (inner) and (b) 10 km s−1. The solid black lines outline the analysed survey coverage.

In the text

	Fig. A.1. RMS noise map. In the plot, the high-value tail of the distribution is cut at ∼0.23 K; the RMS noise values above this cut are mainly found in the regions on the outskirts of the coverage that were not considered in this work.
In the text

Fig. B.1. Distributions of the molecular clouds’ properties (see Sect. 3.3). The panels show: the distributions of a sum of the window function weights, Σiwi, which we used as a proxy for the weighted areal number density (a); the separation between the nearest neighbour clouds, rnnb (b); the absolute value of the nearest neighbour velocity, |vnnb| (c); the virial mass, Mvir (d); size (e); linewidth (f); CO line luminosity, $L_{\text{CO}}^{\prime }$ (g); and the probability (h; see panel d of Fig. 4). The red and grey histograms show the distributions of the cloud properties for the data points inside and outside the 30Dor aperture, respectively. The 30Dor aperture was centred on R136, assuming a radius of 100 pc. The vertical blue lines show the medians of the distributions: 30Dor (dashed) and field (i.e. outside 30Dor; solid). The y axes are shown using a logarithmic scale. We note that the Σiwi, rnnb, |vnnb| distributions show a clear difference between the 30Dor region and the rest of the observed field as opposed to all the other properties analysed.

In the text