Free Access
Issue
A&A
Volume 625, May 2019
Article Number A103
Number of page(s) 19
Section Interstellar and circumstellar matter
DOI https://doi.org/10.1051/0004-6361/201833788
Published online 21 May 2019

© ESO 2019

1 Introduction

High-mass stars (>8 M) play a key role in the evolution of their host galaxies, but their formation is poorly understood, especially for masses >20 M. The leading models of high-mass star formation involve infall from a dense protostellar core, and accretion onto the protostar via a circumstellar disk (Tan et al. 2014; Motte et al. 2018). While rotating disks have been detected around young B-type (Sánchez-Monge et al. 2013; Beltrán & de Wit 2016) and O-type (Johnston et al. 2015; Cesaroni et al. 2017) protostars, the exact manner in (and rate at) which material is gathered from the surroundings is still a matter of debate.

In the “monolithic collapse” model, a massive dense core collapses under its own gravity and forms a (cluster of) protostar(s), much like the low-mass case. This picture is supported by observations of massive collimated outflows from high-mass protostars (Beuther et al. 2002). In the alternative “competitive accretion” model, the accreting protostellar core is replenished from the surroundings. Evidence supporting this model comes, for example, from observations of extended contracting motions in pre-protocluster regions (Pillai et al. 2011). It is possible that both models are valid under different conditions, or that combinationmodels need to be developed (Peters et al. 2011). To constrain such models, observations of suitable tracers are essential.

Large-scale ( ~0.1 pc) infall motions have been detected toward high-mass star-forming regions in ground-based submillimeter-wave molecular emission line maps (Motte et al. 2003; Peretto et al. 2006), in redshifted NH3 line absorption at centimeter wavelengths (Sollins et al. 2005; Beltrán et al. 2006), and recently in SOFIA NH3 spectra (Wyrowski et al. 2012, 2016). Searches for infall in unbiased selections from catalogs of high-mass star-forming regions confirm the ubiquity of such motions (Fuller et al. 2005; Klaassen & Wilson 2007; He et al. 2015; Cunningham et al. 2018).

The water molecule appears to be a promising tracer of infall motions in low-mass star-forming regions (Mottram et al. 2013), and San José-García et al. (2016) linked water observations between low- and high-mass star-forming regions. Spectra of low-J line emission toward high-mass objects often exhibit inverse P Cygni profiles (Van der Tak et al. 2013), which have been modeled successfully as infall, using spherical Monte Carlo models (Herpin et al. 2016). Stronger evidence comes from maps of the luminous mini-starburst region W43 in low-energy H2 O and HO lines (Jacq et al. 2016): extended HO absorption that is redshifted with respect to the 13CO 10–9 emission clearly indicates infall motions.

The HO 111–000 ground-state absorption toward W43-MM1 is remarkable because its shape closely matches that of the central absorption feature in the H2 O 202–111 excited-state emission line (Jacq et al. 2016). This resemblance strongly suggests that the two lines originate in the same gas, which is curious given their different excitation energies (101 vs. 0 K). In order to understand the similarity of these line profiles, this paper explores whether the same effect is seen in other high-mass protostars.

Another puzzle in previous observations of H2O lines toward high-mass protostars concerns their line shape (Van der Tak et al. 2013). The profiles show narrow line cores from the protostellar envelopes, and broad line wings from the outflows, but the wings are much more pronounced at redshifted than at blueshifted velocities, and often the blueshifted wings are nearly or entirely missing from the profiles. This asymmetry cannot be due to continuum absorption (e.g. by a disk) which would preferentially affect background gas (i.e., receding velocities). Special geometrical configurations may explain individual cases, but not a sample of many sources. One possibility is that the H2 O excitation temperatureis close to the brightness temperature of the background, so that no net line emission or absorption appears in the spectra. To explore the origin of the asymmetry, this paper explores its dependence on line properties such as excitation energy and critical density.

This paper uses multi-line maps and spectra of H2 O and HO lines toward a sample of high-mass protostars to explore their gas distribution and dynamics. In particular, we compare HO line profiles to those of C18O to search for velocity shifts due to infall motions. Furthermore, we use H2 O maps to measure the sizes of the protostellar envelopes, and to test the assumption of spherical symmetry in previous analyses of pointed spectra. Section 2 describes our observations, and Sect. 3 presents the resulting maps and spectra. Section 4 compares our derived infall rates with previous observations and with models, and searches for trends with basic source parameters. Finally, Sect. 5 describes our conclusions.

2 Observations

2.1 Source sample

As part of the guaranteed time program WISH (Water In Star-forming regions with Herschel; Van Dishoeck et al. 2011), we have selected 19 regions of high-mass star formation for observation in lines of H2 O and its isotopes with the Heterodyne Instrument for the Far Infrared (HIFI; De Graauw et al. 2010) on ESA’s Herschel Space Observatory (Pilbratt et al. 2010). The sources were selected to cover wide ranges in bolometric luminosity, mid-infrared brightness, and circumstellar mass, and to include regions with hot molecular cores and ultracompact HII regions; see Van der Tak et al. (2013) for details. Table 1 presents the source sample, where distances are updated following König et al. (2017), and luminosities and masses are scaled assuming a simple d2 dependence.

Most of the updated distances are direct determinations using trigonometric maser parallax observations. The near kinematic distance for G327 seems to be broadly accepted in the recent literature. Only the case of G31.41 is more complicated. The commonly used distance for G31.41 is 7.9 kpc, based on its radial velocity from the Sun and position on the sky, coupled with a Galactic rotation model (Churchwell et al. 1990). However, such kinematically derived distances can be off by factors of ≳2 in either direction; AFGL 2591 and W33A are cases in point (Rygl et al. 2012; Immer et al. 2013). Alternatively, G31.41 may be associated with the W43-Main cloud complex, as suggested by position–velocity diagrams of the molecular gas in the surroundings (Nguyen Luong et al. 2011). For W43-Main, two distance estimates exist that are based on Very Long Baseline Interferometry (VLBI) observations of maser parallax (see also Beltrán et al. 2018). Reid et al. (2014) reported a distance of 4.9 kpc to the W43-Main core, while Zhang et al. (2014) reported distances to five maser spots with distances ranging from 6.21 to 4.27 kpc. Given this large spread, we adopt a distance of 4.9 kpc for G31.41 in this paper, and recommend a specific maser parallax study of G31.41 itself.

2.2 Data acquisition and reduction

Maps of the H2 O 202–111 line at 987.927 GHz (hereafter 987 GHz) were taken with HIFI band 4a. The maps are 1′ on the side, and were taken in on-the-fly (OTF) observing mode. The backend was the acousto-optical Wide-Band Spectrometer (WBS) which provides a bandwidth of 4 × 1140 MHz (1200 km s−1) at a resolution of 1.1 MHz (0.3 km s−1). Table 2 presents a detailed observation log including integration times; system temperatures were around 340 K. The FWHM beam size at this frequency is 22″ (Roelfsema et al. 2012), which corresponds to 0.14–0.92 pc at the distances of our sources. The maps thus cover at least part of the protostellar outflows, while the beam resolves the protostellar envelopes, but not any possible disks.

Spectra of the HO 111–000 line at 1101.698 GHz (hereafter 1101 GHz), the H2 O 211–202 line at 752.033 GHz (hereafter 752 GHz), the 13CO 10–9 line at 1101.34976 GHz, and the C18O 9–8 line at 987.560 GHz were obtained toward the same sources with HIFI, using the Double Beam Switch (DBS) observing mode with a chopper throw of 3′. The C18O and 13CO lines were observed in the same tuning as the H2 O 211–202 and the HO 111–000 lines, respectively, and thus share the same ObsIDs. Table 2 lists the integration times of thespectra; system temperatures were around 200 and 390 K for the 752 GHz and ~1 THz lines, respectively. The pointed 987 and 752 GHz spectra have been presented before by San José-García et al. (2016); the 13CO and C18 O spectra were presented in San José-García et al. (2013). The DBS spectra have higher noise per second of integration than the maps at the same frequency, which represents the noise penalty to be paid for stabilizing the system by differencing two reference positions in the DBS observing mode. For the HO and C18 O lines, the beam size of 20–22″ is very similar to that of the 987 GHz maps, which permits a direct comparison of the results. The beam size of the 752 GHz observations is 28″.

The data are Herschel/HIFI standard products (Shipman et al. 2017) with further processing performed in the Herschel Interactive Processing Environment (HIPE; Ott 2010) version 15; further analysis was carried out in the CLASS1 package, version of December 2015 or later. Raw antenna temperatures were converted to Tmb scale using a main beam efficiency of 63% for both frequencies around 1 THz and 64% for the 752 GHz line2 , and linear baselines were subtracted. After inspection, the data from the two polarization channels were averaged to obtain the rms noise levels reported in Table 2. The absolute calibration uncertainty of HIFI bands 3 and 4 is estimated to be 10–15%, but the relative calibration between lines in the same spectrum should be much better, which is relevant for C18 O and 13CO.

Table 1

Source sample.

3 Results

3.1 Line profiles of HO, 13CO, and C18 O

Figure 1 shows the observed velocity profiles of the HO 111–000 and C18O 9–8 lines. For IRAS 18151, we show the H2 O 111–000 line as the HO line is not detected. For IRAS 05358, IRAS 16272, and IRAS 18151, the C18 O 9–8 line is weak, so we use the 13CO 10–9 line to measure velocities. For the other sources, the data indicate substantial optical depth in the 13CO 10–9 line, so we prefer C18O 9–8 as velocity standard.

While the C18O (or 13CO) lines appear purely in emission for all sources, the HO (or H2 O) profiles show absorption, in some cases mixed with emission. Despite this difference, the peak of the HO absorption is seen to lie close to the peak of the C18O (or 13CO) emission, but at a measurable velocity offset. In most cases, the HO absorption peak is significantly redshifted from the C18O (or 13CO) emission peak, by 0.6–3.2 km s−1. Table 3 reports the peak velocities of the HO absorption and C18O (or 13CO) emission, as estimated directly from the HIFI spectra. We estimate the uncertainty on these velocities to be ≈0.3 km s−1. In some cases, no blueshifted absorption or no absorption at all is seen.

The C18O 9–8 line has a relatively high upper level energy (237 K) and critical density (7.7 × 105 cm−3), using spectroscopy from Endres et al. (2016) and collision data from Yang et al. (2010), as provided on the Leiden Atomic and Molecular Database (LAMDA; Schöier et al. 2005). Since in addition the C18 O abundance is likely to be low ( ~10−6), this line should be an optically thin tracer of the warm dense gas close to the central protostar. For the three sources with weak C18 O 9–8 emission, this argument also seems to hold for the 13CO 10–9 line, presumablyowing to a low envelope mass. These three sources are not the lowest luminosity cases in our study, so the low envelope mass and weak C18 O emission may be an evolutionary effect. The appearance of the HO absorption at redshifted velocities thus implies infalling motions in the gas surrounding the dense warm cores seen in C18 O 9–8 and/or 13CO 10–9 emission. The velocity difference between the C18O and HO lines indicates approximate infall speeds between 0.6 and 3.2 km s−1, although these values represent line-of-sight averages.

For thesources W3 IRS5, W33A, NGC 6334I, and IRAS 05358, the HO absorption peak is blueshifted from the C18O emission peak, suggesting expanding motions. The line profiles toward G5.89 and G10.47 are complex, and show a mixture of infall and expansion. These two sources are not included in the analysis below.

We emphasize the importance of using a precise velocity standard, in this case the C18 O 9–8 line, for the detection of infall motions. The C18O velocities in Table 3 differ from the ground-based values (Van der Tak et al. 2013, Table 1; Van Dishoeck et al. 2011) by up to 1 km s−1, which showsthat velocity precision is often limited by source inhomogeneities, rather than by spectral resolution or other instrumental parameters.

Wyrowski et al. (2012, 2016) have used SOFIA to measure the NH3 line at 1810.379 GHz toward several of our sources. These authors reported redshifted absorption toward W43 MM1, G327, G31.41, and G34.26, implying infall, and blueshifted absorption toward W33A and G5.89, thereby implying expansion. These results agree qualitatively with ours and their measured velocities are similar to those reported in this work.

Toward G34.26, Hajigholi et al. (2016) have measured infall through multi-line NH3 line observations with HIFI and found two infall components with velocities of 2.7 and 5.3 km s−1. The ground-state HO and NH3 lines presented in this work and by Wyrowski et al. only probe the lower-velocity of these components, which may mean that the higher-velocity component mostly arises in very warm and dense gas in close proximity to the protostar. This result suggests that the infall velocity of the gas increases as it approaches the protostar.

Table 2

Observation log for the HO pointed observations and H2O 987 GHz maps.

thumbnail Fig. 1

Line profiles of HO 111–000 (black) and C18O 9–8 (red) towardour 19 sources. The vertical green line denotes the C18O velocity in Table 3. For IRAS 18151, we show H2O 111–000 instead of the HO line which is not detected. For IRAS 18151, IRAS 16272 and IRAS 05358, the blue dotted spectrum is 13CO 10–9 as C18 O is weak or noisy. The dip in the G10.47 spectrum at VLSR > 80 km s−1 is an artifact from the image sideband.

Open with DEXTER

3.2 Maps of H2O

Figures 2 and A.1A.17 show our maps of the H2 O 987 GHz line emission. The greyscale and white contours denote the line core, while the blue and red contours correspond to the blue- and redshifted line wings (see the caption for details). The emission is seen to be compact (except G31.41), mildly elongated, and not to depend much on velocity interval (except NGC 6334I). For IRAS 18151, the emission is too weak to assess its morphology. The map of NGC 7538 is not shown as it suffers from mispointing, so that only limits on the emission size and shape can be obtained.

The observed morphology of the 987 GHz emission does not appear to depend much on velocity interval (Figs. 2 and A.1A.17). This contrasts with the low-J CO emission from our sources, which shows a clear bipolar morphology, especially at velocities away from line center (see references in Van Dishoeck et al. 2011). We conclude that the bulk of the warm dense gas in the outflow as traced by the H2 O 987 GHz line is confined to a small volume ( ≲20″) from the source, unlike the outflow gas at lower temperature and density traced by low-J CO lines.

We measured the size of the 987 GHz emission by fitting a two-dimensional Gaussian plus a background offset to the images in Figs. 2 and A.1A.17. Table 4 reports the resulting radii, which have been deconvolved assuming that the source and beam profiles add in quadrature. The measured sizes of the H2 O emission are ~2× smaller than the values measured in high-J CO lines with Herschel (Karska et al. 2014; Kwon et al. 2017), and 2–3× smaller than the sizes of the submillimeter dust emission measured from the ground (Van der Tak et al. 2013). Evidently, the H2 O emission traces warmdense gas close to the protostars.

Comparing the major and minor axis values in Table 4, we see that the H2 O emission is close to spherical in most cases, with axis ratios between 1.1 and 1.4. We conclude that protostellar envelopes dominate the emission, without any evidence for flattening or elongation caused by rotation or bipolar outflows.

Table 4 compares the observed shape of the H2 O 987 GHz emission to the predictions from radiative transfer models, assuming a constant H2 O abundance, following Herpin et al. (2016). These predictions are fits to multi-line H2 O (and isotopic) spectra from HIFI, using the physical structure models from Van der Tak et al. (2013). The predicted size is seen to be 30–40% larger than the observed size for most sources, which we consider good agreement given the simplifying assumption of spherical symmetry in the models. Only for the sources W3 IRS5 and W43 MM1, the predicted size is 2–4 times smaller than the observed size. As with the axis ratios, this may be due to outflows contributingto the emission. Furthermore, the models assume a single central source, whereas interferometric images of our objects often show multiple cores at the center (e.g., Hunter et al. 2014; Brogan et al. 2016; Izquierdo et al. 2018).

The lineintensities in the maps are typically 70–80% of the values reported from pointed observations at the same position. This difference is as expected from the 4% larger beam size due to the OTF observing mode and the spatial regridding, assuming a small emitting area. Only for IRAS 18151 and IRAS 18089, the map intensities are substantially lower ( ≈40% of the pointed observations) for unknown reasons. In such cases, the pointed observations are more reliable, since their calibration is more thorough, with multiple references and longer integrations. We conclude that mapping modes are useful to measure source sizes, but usually underestimate line intensities, sometimes substantially.

Table 3

Measured velocities and derived infall rates.

4 Discussion

4.1 Origin of H2O and HO line emission and absorption

Figure 3 compares the observed HO line profiles with those of the H2O 987 and 752 GHz lines. For the 987 GHz line, we use the pointed observations rather than convolving the map data, because of the calibration issue with the maps (Sect. 3.2) and because the map data have higher noise levels. Remarkably, the HO line profile (shown in black) is very similar to the difference between the two H2 O lines (shown in gray). As found before for the case of W43-MM1 by another method (Jacq et al. 2016), this close similarity implies that the HO absorption originates in warm gas (T ≳ 100 K). Given the upper level energies of the two H2O lines (101 and 137 K), the bulk of the HO absorption must arise in gas with temperatures between ~100 and ~140 K. These temperatures are just above the point where H2O ice sublimates from dust grains, which is expected to lead to a strong increase in the gas-phase H2 O abundance (Boogert et al. 2015). The HO absorption is unlikely to arise in the cold outer envelope, where the H2 O abundance is too low to create detectable absorption in HO (cf. Shipman et al. 2014). The success of the subtraction procedure shows that the outer envelope does not contribute to the HO absorption.

For the sources W3 IRS5, NGC 7538, W33A, AFGL 2591, G29.96, G10.47, and W51N, the subtraction also reproduces HO emission features. Since emission is sensitive to beam filling factors, this similarity is even stronger evidence that the HO line originates between the layers where the 752 GHz line is excited and where the 987 GHz line is excited. In the models by Van der Tak et al. (2013), this zone occurs typically at radii of 1000–5000 au, depending on the luminosity of the source. This region is small enough that it is often difficult to observe (e.g., 2–10″ diameter at a distance of 1 kpc).

In some cases, scaling the 752 GHz profile before subtracting it from the 987 GHz line profile improves the match of the difference to the HO profile (Fig. 4), in particular for the line wings. The scaling factors that best match the observed profiles range from ≈1 for sources with small deconvolved sizes (Table 4) to ≈1.8 for the most extended sources. These values are just as expected from beam size differences between the 987 and 752 GHz spectra, assuming equal excitation temperatures. There may be other pairs of lines whose differences enable us to probe specific layers of the protostellar cores.

Toward several of the more massive sources, the HO line profiles show absorption in the line wings, especially on the blue-shifted side. Clearly, the HO column density is sufficient to absorb even at velocities only seen in the wings. The C18 O 9–8 spectra show no such high-velocity signals, which implies that the H2 O abundance is enhanced in the high-velocity gas (Herpin et al. 2016). For example, the HO spectrum toward NGC 6334 I(N) shows absorption out to at least 15–20 km s−1 from line center, which has no counterpart in C18O. For this source, the integrated HO absorption from the envelope (roughly between –6 and +1 km s−1, which has a counterpart in C18O emission) is approximately equal to that in the high-velocity blue wing. In contrast, the C18 O 9–8 line indicates ≳10× less mass at high velocities, implying an H2O abundance enhancement by more than an order of magnitude.

Similar conclusions hold for the other sources, except for G5.89 and G34.26 for which weak wings are seen on the C18 O 9–8 profiles. The lack of high-velocity C18O 9–8 emission for most sources is not an excitation effect, as low-J C18 O lines do not show wings either (Hatchell et al. 1998; Watson et al. 2003; Gibb et al. 2004; Thomas & Fuller 2007). We conclude that H2 O abundances in high-mass protostellar outflows are ≳10× higher than in the envelopes.

The H2O abundance in these sources thus appears to have at least 3 levels: low in the outer envelope, high in the inner envelope, and very high in the outflow. This is in line with the work of Van der Tak et al. (2010), who used HIFI maps of the DR21 region in 13CO 10–9 and H2 O 111–000 to derive H2O abundances of ~10−10 for the cool outer envelope, ~10−8 for the warm inner envelope, and ~10−6 for the shocked outflowing gas.

thumbnail Fig. 2

Map of the velocity-integrated emission in the H2O 987 GHz line for IRAS 05358. White contours and grayscale image denote velocity-integrated emission over the range indicated by the gray area in the spectrum in the left panel. Red and blue contours denote red- and blueshifted emission, indicated by the red and blue areas in the left panel. The red and blue maps were created by integrating the 987 GHz data cube over a range of 1 FWHM below and above the VLSR of the envelope, denoted by the vertical black line in the spectrum. The integration ranges are offset by 1/2 FWHM from the VLSR to avoid confusion with absorption features. The lowest contour (at the 1σ level) is drawn dashed, the others (in multiples of σ) are drawn solid. The bar in the bottom left corner denotes the HIFI beam size.

Open with DEXTER

4.2 Infallrates and trends

The rightmost column of Table 3 gives estimates of the infall rates onto our sources. These were calculated using (1)

where m(H2) is the mass of the H2 molecule, and the absolute value of the infall speed Vinf is taken from Table 3. Infall motion appears negative as gas is moving toward the center of the reference frame of our models. Given the similarity of the HO absorption profile with the difference of the H2O 987 and 752 GHz profiles (Sect. 4.1), we adopt the radius of the 120 K point in the envelope models from Van der Tak et al. (2013) for R, and the density at that radius for n(H2). These radii vary between 800 and 9000 au, andthe densities from 7 × 105 to 5 × 107 cm−3. Our observed (deconvolved) sizes agree well (within a factor of 2) with the upper end of this range, except for W43 MM1, G10.47, and W51N, where the observed values are larger.

The resulting infall rates (Table 3, right column) are seen to range from ~7 × 10−5 to ~2 × 10−2 M yr. These values are in reasonable agreement with other observations (e.g., König et al. 2017) and with theoretical models (Tan et al. 2014; Motte et al. 2018). They should be considered order of magnitude estimates, because of our simplified treatment assuming spherical symmetry. The observational uncertainty through the measured line velocities is only a ~30% effect. The derived infall rates depend only weakly on the adopted radius and density: the envelopes of our sources have density profiles that drop off approximately as R−2, so that the effects of R and n on tend to cancel each other.

For our subsamples of mid-infrared quiet and –bright HMPOs, Herpin et al. (2016) and Choi (2015) have made detailed models of the H2 O distribution in the protostellar envelope, including simple step functions for the H2 O abundance in the inner and outer envelope. In order to fit the line profiles of H2 O, HO, and HO in the pointed HIFI spectra, Herpin and Choi had to include radial motions in their models. Figure 5 compares their derived infall velocities to the values found in this work; the dashed line indicates 1:1 correspondence. The two types of estimates of the radial velocity are seen to agree qualitatively, both in the sign of the velocity (infall or outflow) and in its magnitude. The simple estimates of the inflow/expansion velocity are on average ≈2× lower than those from the detailed models, although for four sources, they are actually larger. We consider this agreement as reasonable, given the differences between the two approaches.

Since our source sample covers a range of luminosities, envelope masses, and evolutionary stages, we investigated if our derived source sizes, infall velocities, and infall rates show any trends with Lbol, Menv, and age. To estimate the relative ages of our sources, we used the ratio of Lbol/Menv, which is straightforward to compute and appears to be a robust age tracer (Molinari et al. 2016). In addition, we used the presence of hot molecular cores and/or ultracompact HII regions as a sign of a relatively evolved stage. Third, we looked for trends with the virial mass and the ratio Mvir/Menv, proposed asa stability parameter by König et al. (2017). Virial masses are calculated following Giannetti et al. (2014), using line widths from Table A.2 of Van der Tak et al. (2013).

The only significant trend that we find is between the linear sizes of our sources with their virial masses. Since virial mass depends on size, this trend probably just means that the line width is similar for all sources. In addition, the infall rates seem to increase with virial mass and with the evolutionary indicator Lbol/Menv, but the statistical significance of these trends is small. Even the relation with Mvir has a Pearson correlation coefficient ofonly r = 0.58. For a sample size of N = 11, this r-value corresponds to a probability of false correlation of p = 6%, i.e. a ≈2σ significance. We conclude that the accretion rates may increase with circumstellar mass and with evolutionary stage, but that larger source samples are required to test these claims.

Table 4

Observed and deconvolved source sizes (arcsec).

thumbnail Fig. 3

Spectra of the H2O 987 and 752 GHz lines (blue and purple histograms), and their difference (shaded gray histogram), compared with the HO line profile (black) histogram.

Open with DEXTER
thumbnail Fig. 4

As previous figure, for DR21(OH), with the 752 GHz profile scaled to optimize the match to the HO line wings.

Open with DEXTER
thumbnail Fig. 5

Infall velocities estimated from peak shift between HO and C18 O lines vs. values from detailed fits to H2O line profiles (using RATRAN) by Herpin et al. (2016) (green) and Choi (2015) (red). The dashed line denotes 1:1 correspondence.

Open with DEXTER

5 Conclusions

Based on our measured velocity shifts between HO absorption and C18O emission, infall motions appear to be common in the embedded phase of high-mass star formation, at typical accretion rates of ~1 × 10−4 M yr−1. We find a tentative trend that the highest accretion rates occur for the most massive sources, which is globally consistent with current models of high-mass star formation (Tan et al. 2014; Motte et al. 2018). Our data do not allow us to distinguish between such models, however.

In addition, the accretion rates may increase with age, unlike in the low-mass case, for which accretion rates drop from the Class 0 to the Class III stage, and are highly episodic (Dunham et al. 2014). Signs of episodic accretion, which is well established in the low-mass case, have recently been reported for a high-mass star, in the form of mid-infrared variability suggesting accretion “bursts” (Caratti o Garatti et al. 2017).

Our data do not allow us to discern trends within specific types of sources, nor with protostellar luminosity. A study of H2 O line profiles toward a large (N ~ 100) sample is needed to distinguish such trends and to search for episodic behavior. Data from the Herschel open time programs by Bontemps and Wyrowski may be suitable for this purpose. In the future, such studies will be possible with ESA’s SPace Infrared telescope for Cosmology and Astrophysics (SPICA)3 (Roelfsema et al. 2018; Van der Tak et al. 2018) around 2030, and NASA’s Origins Space Telescope (OST)4 (Battersby et al. 2018) around 2040.

Acknowledgements

This paper is dedicated to the memory of Malcolm Walmsley, who passed away on 1 May 2017 at the age of 75. We remember Malcolm as a great source of inspiration, and we will miss his sharp insight and kind manner. The authors thank the WISH team led by Ewine van Dishoeck for inspiring discussions, and the anonymous referee for useful comments on the manuscript. This research has used the following databases: ADS, CDMS, JPL, and LAMDA. HIFI was designed and built by a consortium of institutes and university departments from across Europe, Canada and the US under the leadership of SRON Netherlands Institute for Space Research, Groningen, The Netherlands with major contributions from Germany, France and the USA. Consortium members are: Canada: CSA, U.Waterloo; France: CESR, LAB, LERMA, IRAM; Germany: KOSMA, MPIfR, MPS; Ireland, NUI Maynooth; Italy: ASI, IFSI-INAF, Arcetri-INAF; Netherlands: SRON, TUD; Poland: CAMK, CBK; Spain: Observatorio Astronómico Nacional (IGN), Centro de Astrobiología (CSIC-INTA); Sweden: Chalmers University of Technology – MC2, RSS & GARD, Onsala Space Observatory, Swedish National Space Board, Stockholm University – Stockholm Observatory; Switzerland: ETH Zürich, FHNW; USA: Caltech, JPL, NHSC.

Appendix A Maps of all sources

thumbnail Fig. A.1

As Fig. 2, for IRAS 16272.

Open with DEXTER
thumbnail Fig. A.2

As previous figure, for NGC 6334I(N).

Open with DEXTER
thumbnail Fig. A.3

As previous figure, for W43-MM1.

Open with DEXTER
thumbnail Fig. A.4

As previous figure, for DR21(OH).

Open with DEXTER
thumbnail Fig. A.5

As previous figure, for W3 IRS5.

Open with DEXTER
thumbnail Fig. A.6

As previous figure, for IRAS 18089.

Open with DEXTER
thumbnail Fig. A.7

As previous figure, for W33A.

Open with DEXTER
thumbnail Fig. A.8

As previous figure, for IRAS 18151.

Open with DEXTER
thumbnail Fig. A.9

As previous figure, for AFGL 2591.

Open with DEXTER
thumbnail Fig. A.10

Asprevious figure, for G327.

Open with DEXTER
thumbnail Fig. A.11

Asprevious figure, for NGC 6334I.

Open with DEXTER
thumbnail Fig. A.12

Asprevious figure, for G29.96.

Open with DEXTER
thumbnail Fig. A.13

Asprevious figure, for G31.41.

Open with DEXTER
thumbnail Fig. A.14

Asprevious figure, for G5.89.

Open with DEXTER
thumbnail Fig. A.15

Asprevious figure, for G10.47.

Open with DEXTER
thumbnail Fig. A.16

Asprevious figure, for G34.26.

Open with DEXTER
thumbnail Fig. A.17

Asprevious figure, for W51N.

Open with DEXTER

References

  1. Battersby, C., Armus, L., Bergin, E., et al. 2018, Nat. Astron., 2, 596 [NASA ADS] [CrossRef] [Google Scholar]
  2. Beltrán, M. T., & de Wit, W. J. 2016, A&ARv, 24, 6 [NASA ADS] [CrossRef] [Google Scholar]
  3. Beltrán, M. T., Cesaroni, R., Codella, C., et al. 2006, Nature, 443, 427 [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  4. Beltrán, M. T., Cesaroni, R., Rivilla, V. M., et al. 2018, A&A, 615, A141 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  5. Beuther, H., Schilke, P., Sridharan, T. K., et al. 2002, A&A, 383, 892 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  6. Boogert, A. C. A., Gerakines, P. A., & Whittet, D. C. B. 2015, ARA&A, 53, 541 [NASA ADS] [CrossRef] [Google Scholar]
  7. Brogan, C. L., Hunter, T. R., Cyganowski, C. J., et al. 2016, ApJ, 832, 187 [NASA ADS] [CrossRef] [Google Scholar]
  8. Caratti o Garatti, A., Stecklum, B., Garcia Lopez, R., et al. 2017, Nat. Phys., 13, 276 [NASA ADS] [CrossRef] [Google Scholar]
  9. Cesaroni, R., Sánchez-Monge, Á., Beltrán, M. T., et al. 2017, A&A, 602, A59 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  10. Chavarría, L., Herpin, F., Jacq, T., et al. 2010, A&A, 521, L37 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  11. Choi, Y. 2015, Ph.D. Thesis, University of Groningen [Google Scholar]
  12. Churchwell, E., Walmsley, C. M., & Cesaroni, R. 1990, A&AS, 83, 119 [NASA ADS] [Google Scholar]
  13. Cunningham, N., Lumsden, S. L., Moore, T. J. T., Maud, L. T., & Mendigutía, I. 2018, MNRAS, 477, 2455 [NASA ADS] [CrossRef] [Google Scholar]
  14. De Graauw, T., Helmich, F. P., Phillips, T. G., et al. 2010, A&A, 518, L6 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  15. Dunham, M. M., Stutz, A. M., Allen, L. E., et al. 2014, Protostars and Planets VI (Tucson: University of Arizona Press), 195 [Google Scholar]
  16. Endres, C. P., Schlemmer, S., Schilke, P., Stutzki, J., & Müller, H. S. P. 2016, J. Mol. Spectr., 327, 95 [NASA ADS] [CrossRef] [Google Scholar]
  17. Fuller, G. A., Williams, S. J., & Sridharan, T. K. 2005, A&A, 442, 949 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  18. Giannetti, A., Wyrowski, F., Brand, J., et al. 2014, A&A, 570, A65 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  19. Gibb, A. G., Wyrowski, F., & Mundy, L. G. 2004, ApJ, 616, 301 [NASA ADS] [CrossRef] [Google Scholar]
  20. Hajigholi, M., Persson, C. M., Wirström, E. S., et al. 2016, A&A, 585, A158 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  21. Hatchell, J., Thompson, M. A., Millar, T. J., & MacDonald, G. H. 1998, A&AS, 133, 29 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  22. He, Y.-X., Zhou, J.-J., Esimbek, J., et al. 2015, MNRAS, 450, 1926 [NASA ADS] [CrossRef] [Google Scholar]
  23. Herpin, F., Chavarría, L., Jacq, T., et al. 2016, A&A, 587, A139 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  24. Hunter, T. R., Brogan, C. L., Cyganowski, C. J., & Young, K. H. 2014, ApJ, 788, 187 [NASA ADS] [CrossRef] [Google Scholar]
  25. Immer, K., Reid, M. J., Menten, K. M., Brunthaler, A., & Dame, T. M. 2013, A&A, 553, A117 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  26. Izquierdo, A. F., Galván-Madrid, R., Maud, L. T., et al. 2018, MNRAS, 478, 2505 [NASA ADS] [CrossRef] [Google Scholar]
  27. Jacq, T., Braine, J., Herpin, F., van der Tak, F., & Wyrowski, F. 2016, A&A, 595, A66 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  28. Johnston, K. G., Robitaille, T. P., Beuther, H., et al. 2015, ApJ, 813, L19 [NASA ADS] [CrossRef] [Google Scholar]
  29. Karska, A., Herpin, F., Bruderer, S., et al. 2014, A&A, 562, A45 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  30. Klaassen, P. D., & Wilson, C. D. 2007, ApJ, 663, 1092 [NASA ADS] [CrossRef] [Google Scholar]
  31. König, C., Urquhart, J. S., Csengeri, T., et al. 2017, A&A, 599, A139 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  32. Kurayama, T., Nakagawa, A., Sawada-Satoh, S., et al. 2011, PASJ, 63, 513 [NASA ADS] [Google Scholar]
  33. Kwon, W., van der Tak, F., Karska, A., et al. 2017, A&A, submitted [Google Scholar]
  34. Molinari, S., Merello, M., Elia, D., et al. 2016, ApJ, 826, L8 [NASA ADS] [CrossRef] [Google Scholar]
  35. Motte, F., Schilke, P., & Lis, D. C. 2003, ApJ, 582, 277 [NASA ADS] [CrossRef] [Google Scholar]
  36. Motte, F., Bontemps, S., & Louvet, F. 2018, ARA&A, 56, 41 [NASA ADS] [CrossRef] [Google Scholar]
  37. Mottram, J. C., van Dishoeck, E. F., Schmalzl, M., et al. 2013, A&A, 558, A126 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  38. Nguyen Luong, Q., Motte, F., Schuller, F., et al. 2011, A&A, 529, A41 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  39. Ott, S. 2010, in Astronomical Data Analysis Software and Systems XIX, eds. Y. Mizumoto, K.-I. Morita, & M. Ohishi, ASP Conf. Ser., 434, 139 [NASA ADS] [Google Scholar]
  40. Peretto, N., André, P., & Belloche, A. 2006, A&A, 445, 979 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  41. Peters, T., Banerjee, R., Klessen, R. S., & Mac Low, M.-M. 2011, ApJ, 729, 72 [NASA ADS] [CrossRef] [Google Scholar]
  42. Pilbratt, G. L., Riedinger, J. R., Passvogel, T., et al. 2010, A&A, 518, L1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  43. Pillai, T., Kauffmann, J., Wyrowski, F., et al. 2011, A&A, 530, A118 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  44. Reid, M. J., Menten, K. M., Brunthaler, A., et al. 2014, ApJ, 783, 130 [NASA ADS] [CrossRef] [Google Scholar]
  45. Roelfsema, P. R., Helmich, F. P., Teyssier, D., et al. 2012, A&A, 537, A17 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  46. Roelfsema, P. R., Shibai, H., Armus, L., et al. 2018, PASA, 35, e030 [NASA ADS] [CrossRef] [Google Scholar]
  47. Rygl, K. L. J., Brunthaler, A., Sanna, A., et al. 2012, A&A, 539, A79 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  48. Sánchez-Monge, Á., Cesaroni, R., Beltrán, M. T., et al. 2013, A&A, 552, L10 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  49. San José-García, I., Mottram, J. C., Kristensen, L. E., et al. 2013, A&A, 553, A125 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  50. San José-García, I., Mottram, J. C., van Dishoeck, E. F., et al. 2016, A&A, 585, A103 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  51. Sanna, A., Reid, M. J., Menten, K. M., et al. 2014, ApJ, 781, 108 [NASA ADS] [CrossRef] [Google Scholar]
  52. Sato, M., Reid, M. J., Brunthaler, A., & Menten, K. M. 2010, ApJ, 720, 1055 [NASA ADS] [CrossRef] [Google Scholar]
  53. Schöier, F. L., van der Tak, F. F. S., van Dishoeck, E. F., & Black, J. H. 2005, A&A, 432, 369 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  54. Shipman, R. F., van der Tak, F. F. S., Wyrowski, F., Herpin, F., & Frieswijk, W. 2014, A&A, 570, A51 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  55. Shipman, R. F., Beaulieu, S. F., Teyssier, D., et al. 2017, A&A, 608, A49 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  56. Sollins, P. K., Zhang, Q., Keto, E., & Ho, P. T. P. 2005, ApJ, 624, L49 [NASA ADS] [CrossRef] [Google Scholar]
  57. Tan, J. C., Beltrán, M. T., Caselli, P., et al. 2014, Protostars and Planets VI (Tucson, AZ: University of Arizona Press), 149 [Google Scholar]
  58. Thomas, H. S., & Fuller, G. A. 2007, ApJ, 659, L165 [NASA ADS] [CrossRef] [Google Scholar]
  59. Van der Tak, F. F. S., Marseille, M. G., Herpin, F., et al. 2010, A&A, 518, L107 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  60. Van der Tak, F. F. S., Chavarría, L., Herpin, F., et al. 2013, A&A, 554, A83 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  61. Van der Tak, F. F. S., Madden, S. C., Roelfsema, P., et al. 2018, PASA, 35, e002 [NASA ADS] [CrossRef] [Google Scholar]
  62. Van Dishoeck, E. F., Kristensen, L. E., Benz, A. O., et al. 2011, PASP, 123, 138 [NASA ADS] [CrossRef] [Google Scholar]
  63. Watson, C., Araya, E., Sewilo, M., et al. 2003, ApJ, 587, 714 [NASA ADS] [CrossRef] [Google Scholar]
  64. Wienen, M., Wyrowski, F., Menten, K. M., et al. 2015, A&A, 579, A91 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  65. Wu, Y. W., Sato, M., Reid, M. J., et al. 2014, A&A, 566, A17 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  66. Wyrowski, F., Güsten, R., Menten, K. M., Wiesemeyer, H., & Klein, B. 2012, A&A, 542, L15 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  67. Wyrowski, F., Güsten, R., Menten, K. M., et al. 2016, A&A, 585, A149 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  68. Xu, J.-L., Li, D., Zhang, C.-P., et al. 2016, ApJ, 819, 117 [NASA ADS] [CrossRef] [Google Scholar]
  69. Yang, B., Stancil, P. C., Balakrishnan, N., & Forrey, R. C. 2010, ApJ, 718, 1062 [NASA ADS] [CrossRef] [Google Scholar]
  70. Zhang, B., Moscadelli, L., Sato, M., et al. 2014, ApJ, 781, 89 [NASA ADS] [CrossRef] [Google Scholar]

All Tables

Table 1

Source sample.

Table 2

Observation log for the HO pointed observations and H2O 987 GHz maps.

Table 3

Measured velocities and derived infall rates.

Table 4

Observed and deconvolved source sizes (arcsec).

All Figures

thumbnail Fig. 1

Line profiles of HO 111–000 (black) and C18O 9–8 (red) towardour 19 sources. The vertical green line denotes the C18O velocity in Table 3. For IRAS 18151, we show H2O 111–000 instead of the HO line which is not detected. For IRAS 18151, IRAS 16272 and IRAS 05358, the blue dotted spectrum is 13CO 10–9 as C18 O is weak or noisy. The dip in the G10.47 spectrum at VLSR > 80 km s−1 is an artifact from the image sideband.

Open with DEXTER
In the text
thumbnail Fig. 2

Map of the velocity-integrated emission in the H2O 987 GHz line for IRAS 05358. White contours and grayscale image denote velocity-integrated emission over the range indicated by the gray area in the spectrum in the left panel. Red and blue contours denote red- and blueshifted emission, indicated by the red and blue areas in the left panel. The red and blue maps were created by integrating the 987 GHz data cube over a range of 1 FWHM below and above the VLSR of the envelope, denoted by the vertical black line in the spectrum. The integration ranges are offset by 1/2 FWHM from the VLSR to avoid confusion with absorption features. The lowest contour (at the 1σ level) is drawn dashed, the others (in multiples of σ) are drawn solid. The bar in the bottom left corner denotes the HIFI beam size.

Open with DEXTER
In the text
thumbnail Fig. 3

Spectra of the H2O 987 and 752 GHz lines (blue and purple histograms), and their difference (shaded gray histogram), compared with the HO line profile (black) histogram.

Open with DEXTER
In the text
thumbnail Fig. 4

As previous figure, for DR21(OH), with the 752 GHz profile scaled to optimize the match to the HO line wings.

Open with DEXTER
In the text
thumbnail Fig. 5

Infall velocities estimated from peak shift between HO and C18 O lines vs. values from detailed fits to H2O line profiles (using RATRAN) by Herpin et al. (2016) (green) and Choi (2015) (red). The dashed line denotes 1:1 correspondence.

Open with DEXTER
In the text
thumbnail Fig. A.2

As previous figure, for NGC 6334I(N).

Open with DEXTER
In the text
thumbnail Fig. A.3

As previous figure, for W43-MM1.

Open with DEXTER
In the text
thumbnail Fig. A.4

As previous figure, for DR21(OH).

Open with DEXTER
In the text
thumbnail Fig. A.5

As previous figure, for W3 IRS5.

Open with DEXTER
In the text
thumbnail Fig. A.6

As previous figure, for IRAS 18089.

Open with DEXTER
In the text
thumbnail Fig. A.7

As previous figure, for W33A.

Open with DEXTER
In the text
thumbnail Fig. A.8

As previous figure, for IRAS 18151.

Open with DEXTER
In the text
thumbnail Fig. A.9

As previous figure, for AFGL 2591.

Open with DEXTER
In the text
thumbnail Fig. A.10

Asprevious figure, for G327.

Open with DEXTER
In the text
thumbnail Fig. A.11

Asprevious figure, for NGC 6334I.

Open with DEXTER
In the text
thumbnail Fig. A.12

Asprevious figure, for G29.96.

Open with DEXTER
In the text
thumbnail Fig. A.13

Asprevious figure, for G31.41.

Open with DEXTER
In the text
thumbnail Fig. A.14

Asprevious figure, for G5.89.

Open with DEXTER
In the text
thumbnail Fig. A.15

Asprevious figure, for G10.47.

Open with DEXTER
In the text
thumbnail Fig. A.16

Asprevious figure, for G34.26.

Open with DEXTER
In the text
thumbnail Fig. A.17

Asprevious figure, for W51N.

Open with DEXTER
In the text

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.