# An ALMA study of outflow parameters of protoclusters: outflow feedback to
maintain the turbulence

T. Baug,1,2,3 Ke Wang,1,3 Tie Liu,4 Yue-Fang Wu,5 Di Li, 6,7,8 Qizhou Zhang,9
Mengyao Tang,10 Paul F. Goldsmith,11 Hong-Li Liu,12 Anandmayee Tej,13 Leonardo
Bronfman,14 L. Viktor Toth,15 Kee-Tae Kim,16 Shang-Huo Li,4 Chang Won
Lee,16,17 Ken’ichi Tatematsu,18 and Tomoya Hirota18
1Kavli Institute for Astronomy and Astrophysics, Peking University, 5 Yiheyuan
Road, Haidian District, Beijing 100871, China
2S. N. Bose National Centre for Basic Sciences, Block-JD, Sector-III, Salt
Lake, Kolkata 700106, India
3PKU - SHAO Joint Research Center for Astrophysics, Peking University, 5
Yiheyuan Road, Haidian District, Beijing 100871, China
4Shanghai Astronomical Observatory, Chinese Academy of Sciences, 80 Nandan
Road, Shanghai 200030, China
5Department of Astronomy, Peking University, 100871, Beijing, People’s
Republic of China
6CAS Key Laboratory of FAST, National Astronomical Observatories, Chinese
Academy of Sciences, Beijing 100101, China
7University of Chinese Academy of Sciences, Beijing 100049, China
8NAOC-UKZN Computational Astrophysics Centre, University of KwaZulu-Natal,
Durban 4000, South Africa
9Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA
02138, USA
10Department of Astronomy, Yunnan University, Kunming, 650091, China
11Jet Propulsion Laboratory, National Aeronautics and Space Administration,
United States
12Departamento de Astronomía, Universidad de Concepción, Av. Esteban Iturra
s/n, Distrito Universitario, 160-C, Chile
13Indian Institute of Space Science and Technology, Thiruvananthapuram 695
547, Kerala, India
14Departamento de Astronomía, Universidad de Chile, Casilla 36-D, Santiago,
Chile
15Eövös Loránd University, Department of Astronomy, Pázmány Péter sétány 1/A,
H-1117, Budapest, Hungary
16Korea Astronomy and Space Science Institute, 776 Daedeokdae-ro, Yuseong-gu,
Daejeon 34055, Republic of Korea
17University of Science and Technology, Korea (UST), 217 Gajeong-ro, Yuseong-
gu, Daejeon 34113, Republic of Korea
18National Astronomical Observatory of Japan, National Institutes of Natural
Sciences, 2-21-1 Osawa, Mitaka, Tokyo 181-8588, Japan
E-mail<EMAIL_ADDRESS>(TB)Corresponding Author<EMAIL_ADDRESS>(KW)

(Accepted XXX. Received YYY; in original form ZZZ)

###### Abstract

With the aim of understanding the role of outflows in star formation, we
performed a statistical study of the physical parameters of outflows in eleven
massive protoclusters associated with ultra-compact Hii regions. A total of
106 outflow lobes are identified in these protoclusters using the ALMA CO
(3–2), HCN (4–3) and HCO+ (4–3) line observations. Although the position
angles of outflow lobes do not differ in these three tracers, HCN and HCO+
tend to detect lower terminal velocity of the identified outflows compared to
CO. The majority of the outflows in our targets are young with typical
dynamical time-scales of 10${}^{2}-$104 years, and are mostly composed of low-
mass outflows along with at least one high-mass outflow in each target. An
anti-correlation of outflow rate with dynamical time-scale indicates that the
outflow rate possibly decreases with time. Also, a rising trend of dynamical
time-scale with the mass of the associated core hints that the massive cores
might have longer accretion histories than the low mass cores. Estimation of
different energies in these protoclusters shows that outflows studied here
cannot account for the generation of the observed turbulence, but can sustain
the turbulence at the current epoch as the energy injection rate from the
outflows is similar to the estimated dissipation rate.

###### keywords:

ISM: clouds – ISM: jets and outflows – stars: formation – radio lines: ISM

††pubyear: 2020††pagerange: An ALMA study of outflow parameters of
protoclusters: outflow feedback to maintain the turbulence–12

##  1 Introduction

Star formation is a complex process that involves the collapse and accretion
of gas onto protostars (Lada, 1985). Molecular outflows are ubiquitous in the
early stages of star formation. It is expected that at the protostellar phase
a fraction of the accreted material from the envelope/disk is expelled as a
result of angular momentum conservation (see reviews by Frank et al., 2014;
Bally, 2016). The gas is typically ejected in the form of high-velocity
collimated jets that sweep up the surrounding material and form molecular
outflows around the jet axis (Lee et al., 2000; Arce et al., 2007; Hartmann et
al., 2016, and references therein). Compared to accretion disks, these
outflows are more easily detectable in star-forming clouds (Bally, 2016, and
references therein). Since the first-ever observational detection of a
molecular outflow in Orion A (Kwan & Scoville, 1976; Zuckerman et al., 1976),
many outflows have been found in Galactic star-forming regions. Over the past
few decades, studies of the outflows have increased significantly (see the
review by Bally, 2016), and the jets/outflows were detected in all mass
regimes, starting from brown dwarfs (e.g., Whelan et al., 2005; Riaz et al.,
2017) to intermediate-mass protostars (e.g., Zapata et al., 2010; Reiter et
al., 2017; Takahashi et al., 2019) to high-mass protostars (e.g., Caratti o
Garatti et al., 2015; Li et al., 2019, and references therein).

One of the main debates in the star formation community is whether the massive
young stellar objects are a scaled-up version of low-mass young stellar
objects (YSOs) where disk-accretion plays the dominating role for gaining the
stellar mass. For the formation of low-mass stars, bipolar outflows driven by
the accretion disks are proposed to be the basic formation mechanism
theoretically (Shu et al., 1987), and are also verified observationally (e.g.,
Bontemps et al., 1996; Richer et al., 2000; Arce et al., 2007, and references
therein). However, on the other hand understanding of the formation mechanism
of massive stars is still elusive (Tan et al., 2014). Two major competing
models for massive star formation are (i) core accretion via disk (McKee &
Tan, 2003) and (ii) competitive accretion (Bonnell et al., 2001). The most
obvious way to distinguish between these two models might be the detection of
the accretion disk around massive protostars. But a direct detection of
accreting disk is difficult because the accretion disk is small and short-
lived, and also because of complicated gas dynamics at that scale (Kim &
Kurtz, 2006). Here, the study of the properties of molecular outflows which
are the manifestation of disk-accretion in young sources, could help us to
improve our understanding of the underlying formation process (Shepherd &
Churchwell, 1996; Beuther et al., 2002; Molinari et al., 2002; Arce et al.,
2007). If it is assumed that massive stars do form via an accretion disk
similar to the low-mass stars, they should generate massive and powerful
outflows (see de Villiers et al., 2014, and references therein). A few recent
studies (e.g., de Villiers et al., 2014; Li et al., 2018) indeed found the
applicability of the same scaling between outflow activity and clump masses
for both low-mass and massive objects, suggesting a similar formation
mechanism. However, an extensive study of molecular outflows toward massive
star-forming regions is still lacking owing to their large distances and high
level of clustering. Recent interferometric observations with the ALMA enable
us to target such regions thanks to its high spatial resolution and
sensitivity. Studies of outflows associated with Galactic massive star-forming
regions may provide us clues to better understand the launching mechanism of
molecular outflows, and hence, the underlying star formation mechanism.

The outflows also inject a large amount of mechanical energy into the parent
molecular cloud (Solomon et al., 1981; Lada, 1985; Bachiller, 1996). Such
energetic feedback from young stars may significantly influence the self-
regulation of star formation (Franco, 1983). Feedback may provide the required
turbulence to the parent molecular cloud for stabilizing it against the
gravitational collapse (Shu et al., 1987; Nakamura & Li, 2007; Carroll et al.,
2009; Matzner & Jumper, 2015). The impact of outflows on surrounding gas has
been studied in several massive and low-mass star-forming regions (Arce et
al., 2010; Nakamura et al., 2011; Narayanan et al., 2012; Li et al., 2015;
Feddersen et al., 2020, and references therein). Majority of these studies
reported that the outflows do not have sufficient energy to sustain the
observed turbulence in their parent molecular clouds (see e.g., Li et al.,
2020, and references therein). In a survey on Perseus molecular cloud, Arce et
al. (2010) found that even though outflows have a large impact on the local
clouds near the active star-forming area, the energy from outflows is not
sufficient to produce the observed turbulence in the entire Perseus complex. A
similar result was also found by Narayanan et al. (2012) and Li et al. (2015)
for Taurus region. However, Nakamura et al. (2011) found that the protostellar
outflows play a crucial role in replenishing the supersonic turbulence in
$\rho$ Ophiuchi Main Cloud. Plunkett et al. (2013) also found the outflow
energy comparable to the turbulent energy in NGC 1333 star-forming region. A
similar result was also obtained by Yang et al. (2018) in hundreds of star-
forming clumps.

Protostellar outflows have been extensively studied using several different
atomic and molecular tracers at different wavelength regime, for example, H2,
[Feii], CO, SiO, HCN, HCO+, HNCO, CS, and so on (see Bally, 2016, for a
detailed discussion). The emission lines of CO, particularly the low-$J$ pure
rotational transitions ($J\leq$4) in millimeter and sub-millimeter bands are
widely used as tracers of outflow activity (Shepherd & Churchwell, 1996;
Beuther et al., 2002; Yang et al., 2018). This is because of the high
abundance of CO and also because observations of these CO lines are relatively
straightforward using ground-based telescopes. In this paper, we report a
study of outflow parameters derived from the CO ($J$ = 3–2) line toward 11
Galactic massive protoclusters (1–24$\times$103 M⊙; Liu et al., 2016) using
data from ALMA. The details of these 11 target regions have been presented in
Table 1 of our previous paper (Baug et al., 2020, hereafter Paper I). In
total, we identified 106 lobes in these 11 target regions. A study of the
orientation of the outflow lobes with respect to their host filaments is
already reported in Paper I. The present paper concentrates on the derived
outflow parameters (e.g., outflow mass, outflow rate, mechanical luminosity,
dynamical timescale of outflow, etc.), their relation with the mass of the
host cores, and the energy injection by these outflows to their parent clouds.

The paper is organized in the following manner. In Section 2, we present a
brief introduction to the data. Identification of outflow lobes, comparison of
the outflow parameters obtained with different tracers (i.e., CO(3–2),
HCN(4–3), HCO+ (4–3); hereafter CO, HCN and HCO+ throughout the paper), and
derivation of physical parameters of the CO outflows are presented in Section
3. A comparison of the CO outflow parameters with the parameters of the host
cores and analysis on the energy budget of the host clouds are presented in
Section 4. A conclusion of this study is presented in Section 5.

##  2 Data

###  2.1 ALMA observations

Details of the ALMA observations (project 2017.1.00545.S; PI: Tie Liu) have
been presented in Paper I. These data were observed as a pilot project for the
ALMA Three-millimeter Observations of Massive Star-forming regions (ATOMS)
survey (Liu et al., 2020a, b).

##  3 Results

###  3.1 Identification of Outflows

The procedure to identify outflows adopted in this work was already reported
in Paper I. For clarity, we briefly mention the procedure here. First, we
cropped the data for the whole spectral window into smaller cubes that only
cover $\pm$200 km s-1 centering on the systemic velocity of each target. We
carefully examined these small position-position-velocity (PPV) data cubes in
SAOImageDS9111https://sites.google.com/cfa.harvard.edu/saoimageds9 looking for
the redshifted and blueshifted lobes around continuum sources starting from
the highest velocity channels, as these channels are expected to be least
contaminated by emission from the central clouds. The outflow lobes in a
couple of regions are substantially crowded. There were a few confusing CO
outflow lobes that overlap with each other. For those, we compared the outflow
lobes in different tracers (i.e., CO, HCN, HCO+, and SiO) to confirm their
credibility. However, in this paper we do not include any analysis from SiO
observations. Analysis using this particular line will be reported for the
full ATOMS survey sample in a future paper. Here, we considered as separate
outflow lobes only those for which the emission between the two lobes at
terminal velocity is separated by more than 5 pixels in the PPV space.

The terminal velocity and extent of each outflow lobe were considered up to a
5$\sigma$ level where $\sigma$ is the rms measured from a few line-free
channels. In addition to the bipolar outflows, we identified unipolar outflows
that are associated with continuum sources, and also a few outflow lobes
without having any association with detected continuum sources. Here, we
report a total of 106 outflow lobes. Among them 32 are bipolar and 42 are
unipolar. Note that in this paper, we report one additional lobe which was
confused and thought to be associated with another lobe in Paper I.

A total of 99 and 80 outflow lobes are detected in HCN and HCO+, respectively.
Among the 106 lobes identified in CO, 80 are identified in HCN and 62 are
identified in HCO+. We also named the outflow lobes for convenience (see
Tables 1 and 2). The naming of an outflow is done as follows. It contains the
name of the region, followed by an alphanumeric character (that starts with
the letter ‘O’) to mark the source number, and a letter if multiple outflow
lobes are identified with a single core. Details of all the identified outflow
lobes, such as coordinates of the continuum source, orientations of outflow
lobes on the plane of the sky, terminal velocity, and extent of each outflow
are presented in Paper I and are thus not presented here. The outflow lobes
identified with HCN overlaid on the 0.9 mm ALMA continuum maps for one region
are presented in Figure 1. Figures for the identified outflows (in both HCN
and HCO+) in the remaining regions are presented in Appendix A. The outflow
lobes identified in CO are also marked by arrows in Figure 1. For reference,
similar figures corresponding to the CO outflows are available in Paper I.

Figure 1:  HCN outflow lobes toward the IRAS 14382-6017 region overlaid on the
ALMA 0.9 mm continuum map of the region. The red and blue contours correspond
to redshifted and blueshifted HCN emission integrated over carefully selected
velocity ranges to depict the outflow lobes. The extents of blueshifted and
redshifted outflow lobes in HCN are also marked by solid green and purple
arrows, while the extent of outflow lobes identified using CO are shown by
blue and red dashed arrows, respectively. The driving sources detected in the
continuum map are marked in yellow ellipses.

###  3.2 Comparison of outflow lobes

In order to examine how the detected outflow lobes conform in three different
tracers, we plot the position angles, velocities, and extents of the outflow
lobes in Figures 2 and 3.

As seen in Figure 2, the position angles of outflow lobes do not differ much
in all three tracers and have typical variations $<\pm$ 10∘. This particular
variation is of the order of uncertainties in the measurements of the position
angles. Figures 3a and 3b show the projected extent of the outflow lobes in
HCN and HCO+ against CO emission, and the extent of the lobes against the
terminal velocities of the outflow lobes in different tracers, respectively.
Even though the extents of the lobes are generally found to be similar in CO
and HCN (Figure 3a), a few HCO+ lobes are found to be 10-20% shorter compared
to the other two tracers. Also, HCN and HCO+ typically have lower terminal
velocities compared to those traced in CO. A similar result was noted earlier
by López-Sepulcre et al. (2010). These authors found lower values of outflow
momenta and kinetic energies estimated using HCO+ (1–0) emission compared to
the commonly used tracers like CO. Typically, HCN and HCO+ trace the outflow
in the vicinity of the driving source, while CO is sensitive enough to detect
the low density and high-velocity outflow material. This is possibly because
CO is more abundant than HCN and HCO+, and also because the excitation of HCN
(4–3) and HCO+ (4–3) requires relatively higher temperatures compared to CO
(3–2). Note that different tracers trace various components of the outflows,
and that explains the variation seen in the observed outflow parameters in
three different tracers (see Bally, 2016, for a detailed discussion).

Figure 2: Comparison of the position angles of the lobes in HCN (black
squares) and HCO+ (red circles) with respect to the position angles of the
lobes in CO. The blue dashed line shows the ratio PA${}_{\rm
HCN/HCO^{+}}$/PACO = 1. Grey dotted lines show the track for PA variation of
$\pm$10∘.

Figure 3: (a) Comparison between the extent of the outflow lobes in HCN and
HCO+ with respect to the extent of the outflow lobes in CO. (b) Comparison of
the extent of the outflow lobes with the terminal velocity of the outflow. In
both the panels, black square and red circles represent parameters for HCN and
HCO+, respectively, while the blue triangles in the bottom panel represent the
values for CO. The blue, black and red dashed lines are drawn ‘by-eye’ for
visualization (no fit involved) to portray typical limits and trends of
terminal velocities and the extent of the lobes in CO, HCN and HCO+,
respectively.

###  3.3 Dynamical properties of outflows

In this paper, we only calculated the parameters for the outflows that are
identified in CO. The calculation of outflows parameters using HCN and HCO+
are not performed in this paper. The physical properties of all the identified
CO outflow lobes, such as mass ($M_{\rm out}$), momentum ($P_{\rm out}$),
energy ($E_{\rm out}$), dynamical ages, outflow rate ($\dot{M}_{\rm out}$),
mechanical luminosity ($L_{\rm mech}$), and outflow force ($F_{\rm out}$) were
derived following the similar procedure described in Wang et al. (2011). We
first simplified the equation given in Mangum & Shirley (2015) to calculate
the total CO column density of the outflows assuming that they are in local
thermodynamic equilibrium and the emission is optically thin. Note that the
calculated mass considering the emission to be optically thin might be
underestimated by a factor of a few (see Offner et al., 2011; Arce et al.,
2013; Dunham et al., 2014). However, opacity correction for the estimated mass
of the outflows is primarily crucial if the lower bound velocity of the lobes
are below 3 km s-1 (see Dunham et al., 2014, for a detailed discussion). We do
not apply any opacity correction as the lower bound velocity of our idenntifed
lobes are typically $\sim$5 km s-1. The equation for the total CO column
density is given by :

$N^{\rm{thin}}_{\rm{CO}}({\rm cm^{-2}})=4.81\times 10^{12}(T_{\rm
ex}+0.92){\rm exp}\left(\frac{33.12}{T_{\rm ex}}\right)\int T_{\rm B}dv,$ (1)

where $T_{\rm ex}$ and $T_{\rm B}$ are excitation temperature and brightness
temperature in K, respectively, and $dv$ is in km s-1. Estimated outflow
parameters may vary depending on the adopted $T_{\rm ex}$. Dunham et al.
(2014) performed a detailed calculation to estimate the correction factor for
a $T_{\rm ex}$ range from 10–200 K. They pointed out that low-$J$ transitions
of CO (as in this study) are generally insensitive for $T_{\rm ex}\gtrsim$ 50
K. Note that the targets in this study are massive star-forming regions
associated with Hii regions. Thus, a higher gas temperature is expected in
these regions compared to the gas temperature typically assumed for cold
clumps (10–30 K). Hence, we derived the outflow parameters in our target
regions assuming a $T_{\rm ex}$ of 50 K. Also, this is a crude assumption to
consider a single $T_{\rm ex}$ for all the outflows. In fact, excitation
temperatures may even vary over different parts of the lobes, and there may be
very warm molecular gas in shocks (see e.g., Green et al., 2011, and
references therein). Thus, consideration of a single $T_{\rm ex}$ can lead to
significant underestimates (by up to factors of 3–4) in the outflow kinetic
energy and mechanical luminosity which are the most sensitive to the highest-
velocity gas (see Downes & Cabrit, 2007; Dunham et al., 2014, for a detail
discussion). As discussed by Dunham et al. (2014), a different value of
$T_{\rm ex}$ from 10–50 K might lead to a maximum variation in the outflow
parameter by a factor of 1–3 which is of the order of uncertainty of these
derived parameters. Here, we derived the outflow parameters using the
equations given in Wang et al. (2011):

$\displaystyle M_{\rm out}$ $\displaystyle=d^{2}\left[\frac{{\rm H_{2}}}{{\rm
CO}}\right]\overline{m}_{\rm
H_{2}}\int_{\Omega}N^{\rm{thin}}_{\rm{CO}}(\Omega^{\prime})d\Omega^{\prime},$
(2) $\displaystyle P_{\rm out}$ $\displaystyle=M_{\rm out}v,$ (3)
$\displaystyle E_{\rm out}$ $\displaystyle=\frac{1}{2}M_{\rm out}v^{2},$ (4)
$\displaystyle t_{\rm dyn}$ $\displaystyle=\frac{L_{\rm flow}}{v_{\rm Lobe}},$
(5) $\displaystyle\dot{M}_{\rm out}$ $\displaystyle=\frac{M_{\rm out}}{t_{\rm
dyn}},$ (6) $\displaystyle L_{\rm mech}$ $\displaystyle=\frac{E_{\rm
out}}{t_{\rm dyn}},$ (7) $\displaystyle F_{\rm out}$
$\displaystyle=\frac{P_{\rm out}}{t_{\rm dyn}},$ (8)

where $d$ is the distance to the source. $\left[\frac{{\rm H_{2}}}{{\rm
CO}}\right]$ is the abundance ratio of $H_{2}$ to CO which is taken to be
$10^{4}$ (Blake et al., 1987). The mean molecular weight, $\overline{m}_{\rm
H_{2}}$, is assumed to be 2.33 $m_{\rm H}$. The parameter $\Omega$ is the
solid angle subtended by the outflow lobe, and $v$ is the velocity of the
outflow relative to the systemic velocity. The parameters $L_{\rm flow}$ and
$v_{\rm Lobe}$ are the extent of the outflow lobe from the driving source and
the outflow terminal velocity, respectively.

Since we only measure the radial component of the outflow velocity and the
projected size of the lobe on the plane of the sky, it is important to include
the corrections for the inclination. We have corrected the parameters, $L_{\rm
flow}$, $P_{\rm out}$, $E_{\rm out}$, $t_{\rm dyn}$, $\dot{M}_{\rm out}$,
$L_{\rm mech}$, and $F_{\rm out}$ multiplying by 1/sin $i$, 1/cos $i$,
1/cos${}^{2}~{}i$, cos $i$/sin $i$, sin $i$/cos $i$, sin $i$/cos${}^{3}~{}i$,
and sin $i$/cos${}^{2}~{}i$, respectively, for a mean inclination angle ($i$)
of 57$\aas@@fstack{\circ}$3 assuming all orientations are equally favorable
(see Dunham et al., 2014, for a detailed description). However, the outflow
parameters estimated here should be treated only as lower limits because it
might include several observational biases. The assumption for the CO line to
be optically thin might not always be true, and unlike other low-density gas
tracers (e.g., Cii 157 $\mu$m line), CO is unable to trace the outflows in
low-density regions. Also, a lower limit may be due to the limited sensitivity
of our observations. Moreover, the lower bound velocity of our identified
outflows is at about 5 km s-1 from the systemic velocity of the cloud because
we could not separate out the outflow gas component from the host cloud below
this velocity limit. However, this low velocity part of the outflow might have
a significant contribution to the observed outflow mass (Arce & Goodman, 2001;
Downes & Cabrit, 2007; Offner et al., 2011). Beside, it was not always
possible to disentangle the overlapping emission from two nearby lobes,
particularly when they approached the cloud velocity (within 2-6 km s-1).
Although, the minimum detectable velocity of our identified outflows is at
about 5 km s-1 from the cloud velocity, this effect might lead to 15-20%
uncertainties to the estimated outflow mass, and hence, to other calculated
parameters. Together all these biases may lead to underestimate the estimated
parameter up to a factor of $\sim$10 even after correcting the estimated
parameters for the mean inclination (Dunham et al., 2014). Table 1 lists the
velocity range used to derive the calculations, $M_{\rm out}$, and several
other calculated dynamical parameters (i.e., $P_{\rm out}$, $E_{\rm out}$,
$\dot{M}_{\rm out}$, $F_{\rm out}$, and $L_{\rm mech}$); the values are listed
for all the outflows after correcting for the inclination. As mentioned
before, in this paper we estimated the outflow parameters based on the CO
emission. So, in Table 1 we include only the outflows detected in CO. The
lobes identified in HCN or HCO+ but not detected in CO are not listed in the
table. Additionally, in Table 1 we indicate whether the corresponding outflow
is detected in either of the other two tracers. For reference, all the outflow
lobes detected in all three tracers are marked in Figures 1 and 9–12.

Figure 4: (a) Histogram of the outflow rate. (b) Histogram of the estimated
dynamical ages of all the outflows, and (c) Histogram of the calculated mass
of the host cores considering a dust temperature of 50 K and spectral index,
$\beta$, of 2.

A histogram of $\dot{M}_{\rm out}$ is shown in Figure 4a. It can be seen that
the values $\dot{M}_{\mathrm{out}}$ have a wide range starting from $\sim$10-7
to $\sim$10-3 $M_{\odot}$ yr-1. This provides us a unique opportunity to study
the behavior of young outflows in a wide range of mass in Galactic massive
protoclusters. Several authors reported that outflows associated with massive
star formation are comparatively more powerful than those associated with low-
mass star formation (Shepherd & Churchwell, 1996; Beuther et al., 2002; Wu et
al., 2004; Hatchell et al., 2007; Yang et al., 2018; Li et al., 2018). While
outflows with $M_{\rm out}$ of 0.1–1 $M_{\odot}$ and $\dot{M}_{\rm out}$ of
10-7–10-6 $M_{\odot}$ yr-1 are known as low-mass outflows, the outflows with
$M_{\rm out}$ of 10–1000 $M_{\odot}$ and $\dot{M}_{\rm out}$ of 10-5–10-3
$M_{\odot}$ yr-1 are considered as high-mass outflows (Yang et al., 2018).
Thus, a wide spread in the values of physical parameters for our observed
outflows indicates that these correspond to low-mass to high-mass outflows.
Note that the targets in this study are massive star-forming regions. We
indeed found at least one massive outflow with $\dot{M}_{\mathrm{out}}$ more
than about 10-4 $M_{\odot}$ yr-1 in almost every region (except for I14382 and
I17204).

A histogram for the dynamical age of the outflows are also shown in Figure 4b.
The dynamical ages of the outflows in our sample are ranging from
0.1–2.8$\times$104 years. These results confirm their driving sources to be
young and still might be in an early phase when the matter is accreted onto
the protostellar condensation.

Table 1: Outflow parameters (listed only those detected in CO) derived from CO Lobe Name |  $v$ (km s-1) | $M_{\rm out}$ |  $P_{\rm out}$ (10-1 | $E_{\rm out}$ | $t_{\mathrm{dyn}}$ |  $\dot{M}_{\mathrm{out}}$ (10-5 |  $F_{\mathrm{out}}$ (10-4 M⊙ | $L_{\mathrm{mech}}$ | Detected in |  WSa
---|---|---|---|---|---|---|---|---|---|---
|  |  |  (10-2 $M_{\odot}$) |  M⊙ km s-1) |  ($M_{\odot}$ km2 s-2) |  (103 yr) |  M⊙ yr-1) |  km s-1 yr-1) |  (10${}^{-1}L_{\odot}$) | HCN |  HCO+ |
| Blue | Red | Blue | Red | Blue | Red | Blue | Red | Blue | Red | Blue | Red | Blue | Red | Blue | Red | B R | B R |
I14382_O1a | [-103, -65] | [ -55, -14] | 0.5 | 0.2 | 1.8 | 0.6 | 4.2 | 1.5 | 0.9 | 0.5 | 0.5 | 0.3 | 2.0 | 1.2 | 3.9 | 2.3 | ✓ ✓ | ✗ ✗ | Y
I14382_O1b | – | [ -55, -8] | – | 0.2 | – | 1.1 | – | 3.5 | – | 1.7 | – | 0.1 | – | 0.6 | – | 1.7 | – ✓ | – ✗ | N
I14382_O2 | [-129, -65] | [ -55, 6] | 1.0 | 0.5 | 3.7 | 2.2 | 10.3 | 6.3 | 0.6 | 0.6 | 1.7 | 0.8 | 6.1 | 3.7 | 14.1 | 8.7 | ✓ ✓ | ✓ ✓ | Y
I14382_O3 | – | [ -55, -32] | – | 0.1 | – | 0.2 | – | 0.4 | – | 1.7 | – | 0.0 | – | 0.1 | – | 0.2 | – ✓ | – ✗ | N
I14498_O1a | – | [ -45, 65] | – | 8.1 | – | 23.0 | – | 73.3 | – | 1.4 | – | 5.9 | – | 16.6 | – | 43.7 | – ✓ | – ✗ | y
I14498_O1b | [-112, -55] | – | 16.8 | – | 43.9 | – | 77.2 | – | 1.8 | – | 9.2 | – | 24.1 | – | 35.1 | – | ✓ – | ✓ – | y
I14498_O2 | [-116, -55] | [ -45, 11] | 5.7 | 0.8 | 15.5 | 2.0 | 31.8 | 4.3 | 0.5 | 0.5 | 11.5 | 1.5 | 31.7 | 3.8 | 53.6 | 6.9 | ✗ ✗ | ✗ ✗ | N
I14498_O3 | – | [ -45, -34] | – | 2.4 | – | 3.5 | – | 2.8 | – | 6.9 | – | 0.3 | – | 0.5 | – | 0.3 | – ✓ | – ✗ | y
I14498_O4 | – | [ -45, -28] | – | 1.5 | – | 2.6 | – | 2.6 | – | 3.7 | – | 0.4 | – | 0.7 | – | 0.6 | – ✗ | – ✗ | y
I15520_O1a | [ -70, -46] | [ -36, -16] | 0.9 | 5.8 | 2.6 | 11.4 | 4.3 | 13.0 | 4.0 | 7.0 | 0.2 | 0.8 | 0.7 | 1.6 | 0.9 | 1.5 | ✓ ✓ | ✓ ✓ | Y
I15520_O2a | – | [ -36, -2] | – | 0.1 | – | 0.4 | – | 0.9 | – | 4.2 | – | 0.0 | – | 0.1 | – | 0.2 | – ✗ | – ✗ | y
I15520_O2b | [ -70, -46] | – | 8.0 | – | 21.8 | – | 33.2 | – | 4.5 | – | 1.8 | – | 4.8 | – | 6.0 | – | ✓ – | ✓ – | y
I15520_O3 | [ -61, -46] | – | 2.0 | – | 4.3 | – | 4.9 | – | 7.3 | – | 0.3 | – | 0.6 | – | 0.6 | – | ✓ – | ✓ – | N
I15520_O4 | [ -99, -46] | – | 1.2 | – | 7.9 | – | 30.3 | – | 2.7 | – | 0.4 | – | 2.9 | – | 9.2 | – | ✗ – | ✗ – | N
I15520_O5 | [-106, -46] | – | 3.0 | – | 11.6 | – | 32.4 | – | 1.8 | – | 1.7 | – | 6.6 | – | 15.2 | – | ✓ – | ✓ – | N
I15596_O1a | [-119, -79] | [ -69, -36] | 5.2 | 7.0 | 15.3 | 19.4 | 30.1 | 35.4 | 5.4 | 7.2 | 1.0 | 1.0 | 2.8 | 2.7 | 4.6 | 4.1 | ✓ ✓ | ✗ ✓ | N
I15596_O1b | [-125, -79] | [ -69, -44] | 6.7 | 2.2 | 18.6 | 4.8 | 36.0 | 6.5 | 2.9 | 3.7 | 2.3 | 0.6 | 6.5 | 1.3 | 10.4 | 1.4 | ✓ ✓ | ✗ ✓ | N
I15596_O2a | [-122, -79] | [ -69, 10] | 3.6 | 5.1 | 10.5 | 18.4 | 20.6 | 59.4 | 0.8 | 0.6 | 4.4 | 9.1 | 13.0 | 32.8 | 21.0 | 87.8 | ✓ ✓ | ✓ ✓ | N
I15596_O2b | [-155, -79] | [ -69, -5] | 1.6 | 3.0 | 7.0 | 9.5 | 24.9 | 26.1 | 0.3 | 0.4 | 5.9 | 7.6 | 25.6 | 24.4 | 75.3 | 55.7 | ✓ ✓ | ✓ ✓ | N
I15596_O3 | – | [ -69, -22] | – | 1.7 | – | 5.5 | – | 14.5 | – | 1.4 | – | 1.2 | – | 4.0 | – | 8.6 | – ✗ | – ✗ | y
I15596_O4 | [-113, -79] | [ -69, -28] | 3.4 | 1.1 | 9.3 | 2.7 | 16.6 | 5.5 | 3.0 | 1.6 | 1.1 | 0.7 | 3.1 | 1.8 | 4.6 | 2.9 | ✓ ✓ | ✓ ✓ | Y
I15596_O5 | [-166, -79] | [ -69, 26] | 7.0 | 6.8 | 45.8 | 51.0 | 225.1 | 266.6 | 1.7 | 1.0 | 4.1 | 7.0 | 26.6 | 52.9 | 108.4 | 228.8 | ✗ ✓ | ✗ ✓ | Y
I15596_O6 | [-122, -79] | [ -69, -5] | 3.8 | 0.5 | 9.6 | 3.2 | 16.6 | 14.4 | 1.8 | 1.3 | 2.1 | 0.4 | 5.3 | 2.5 | 7.6 | 9.2 | ✓ ✓ | ✓ ✓ | Y
I15596_O7 | [-104, -79] | – | 0.0 | – | 0.1 | – | 0.3 | – | 2.4 | – | 0.0 | – | 0.1 | – | 0.1 | – | ✗ – | ✗ – | y
I15596_O8 | [-153, -79] | – | 5.6 | – | 20.4 | – | 55.6 | – | 1.3 | – | 4.2 | – | 15.3 | – | 34.5 | – | ✓ – | ✓ – | y
I16060_O1 | [-120, -97] | [ -81, -52] | 63.3 | 35.4 | 208.2 | 120.8 | 373.7 | 227.1 | 4.1 | 7.6 | 15.6 | 4.6 | 51.3 | 15.8 | 76.3 | 24.6 | ✓ ✓ | ✓ ✓ | N
I16060_O2 | [-136, -97] | – | 69.7 | – | 274.3 | – | 606.8 | – | 6.1 | – | 11.4 | – | 44.8 | – | 82.2 | – | ✓ – | ✓ – | N
I16060_O3 | – | [ -81, -53] | – | 3.3 | – | 9.3 | – | 15.2 | – | 6.7 | – | 0.5 | – | 1.4 | – | 1.9 | – ✗ | – ✗ | y
I16060_O4 | – | [ -81, -66] | – | 0.8 | – | 2.1 | – | 2.9 | – | 15.9 | – | 0.1 | – | 0.1 | – | 0.2 | – ✗ | – ✗ | y
I16071_O1a | [-139, -94] | [ -78, -19] | 3.9 | 18.1 | 19.3 | 63.8 | 56.4 | 155.7 | 4.3 | 5.1 | 0.9 | 3.5 | 4.5 | 12.5 | 10.8 | 25.2 | ✓ ✓ | ✓ ✓ | N
I16071_O1b | [-118, -94] | [ -78, -58] | 11.1 | 8.3 | 35.2 | 22.6 | 64.6 | 35.1 | 6.7 | 5.3 | 1.6 | 1.6 | 5.2 | 4.2 | 8.0 | 5.5 | ✓ ✓ | ✗ ✓ | N
I16071_O1c | [-122, -94] | [ -78, -37] | 4.1 | 12.4 | 11.3 | 36.2 | 18.8 | 64.1 | 2.5 | 2.5 | 1.6 | 4.9 | 4.5 | 14.3 | 6.2 | 21.0 | ✓ ✓ | ✓ ✓ | Y
I16071_O1d | [-225, -94] | – | 40.0 | – | 313.4 | – | 2017.0 | – | 1.1 | – | 37.8 | – | 295.7 | – | 1576.3 | – | ✗ – | ✗ – | y
I16071_O1e | [-117, -94] | – | 12.0 | – | 33.5 | – | 53.1 | – | 3.8 | – | 3.2 | – | 8.8 | – | 11.6 | – | ✓ – | ✓ – | y
I16071_O2 | [-192, -94] | [ -78, 58] | 4.8 | 3.8 | 41.2 | 32.5 | 241.3 | 245.1 | 0.3 | 0.2 | 18.6 | 16.7 | 159.3 | 142.1 | 772.5 | 887.9 | ✓ ✓ | ✗ ✓ | Y
I16071_O3 | [-111, -94] | [ -78, -58] | 6.5 | 1.8 | 19.4 | 4.5 | 31.7 | 6.2 | 3.4 | 7.5 | 1.9 | 0.2 | 5.8 | 0.6 | 7.8 | 0.7 | ✓ ✓ | ✓ ✓ | Y
I16071_O4 | [-102, -94] | – | 2.3 | – | 5.0 | – | 5.8 | – | 9.8 | – | 0.2 | – | 0.5 | – | 0.5 | – | ✓ – | ✓ – | y
I16071_O5 | – | [ -78, -58] | – | 1.2 | – | 3.3 | – | 5.3 | – | 3.7 | – | 0.3 | – | 0.9 | – | 1.2 | – ✗ | – ✗ | y
I16071_O6a | [-177, -94] | [ -78, -56] | 4.4 | 0.8 | 25.6 | 2.5 | 96.3 | 4.2 | 0.3 | 1.0 | 14.0 | 0.8 | 82.0 | 2.5 | 255.5 | 3.5 | ✓ ✓ | ✓ ✓ | N
I16071_O6b | [-148, -94] | [ -78, -2] | 10.5 | 13.7 | 59.8 | 71.8 | 202.9 | 275.0 | 1.4 | 1.0 | 7.8 | 13.2 | 44.2 | 69.1 | 124.2 | 219.2 | ✓ ✓ | ✓ ✓ | N
I16071_O7 | [-173, -94] | – | 11.1 | – | 61.9 | – | 262.4 | – | 1.6 | – | 7.0 | – | 38.8 | – | 136.2 | – | ✓ – | ✓ – | y
I16076_O1a | [-125, -97] | [ -77, -5] | 4.3 | 6.3 | 16.1 | 37.9 | 33.2 | 155.3 | 1.2 | 1.0 | 3.5 | 6.2 | 13.3 | 37.8 | 22.7 | 128.3 | ✓ ✓ | ✓ ✓ | N
I16076_O1b | [-111, -97] | [ -77, 20] | 7.5 | 4.3 | 23.3 | 21.4 | 37.9 | 78.0 | 2.7 | 0.8 | 2.7 | 5.3 | 8.5 | 26.3 | 11.4 | 79.5 | ✓ ✓ | ✗ ✓ | N
I16076_O1c | – | [ -77, -22] | – | 5.7 | – | 25.7 | – | 75.7 | – | 1.5 | – | 3.9 | – | 17.4 | – | 42.4 | – ✓ | – ✓ | N
I16076_O1d | [-171, -97] | [ -77, -10] | 0.8 | 1.9 | 4.1 | 10.0 | 15.2 | 32.5 | 0.8 | 0.5 | 1.1 | 3.8 | 5.3 | 19.6 | 16.3 | 52.5 | ✗ ✗ | ✗ ✗ | N
I16076_O1e | – | [ -77, -55] | – | 0.5 | – | 1.4 | – | 2.3 | – | 3.2 | – | 0.1 | – | 0.4 | – | 0.6 | – ✓ | – ✗ | y
I16076_O1f | – | [ -77, -55] | – | 0.1 | – | 0.3 | – | 0.5 | – | 3.8 | – | 0.0 | – | 0.1 | – | 0.1 | – ✗ | – ✗ | y
I16076_O1g | [-147, -97] | [ -77, -55] | 1.8 | 1.5 | 7.8 | 4.3 | 21.9 | 6.5 | 1.0 | 3.6 | 1.8 | 0.4 | 8.0 | 1.2 | 18.6 | 1.5 | ✓ ✓ | ✓ ✓ | N
I16076_O1h | [-115, -97] | [ -77, -58] | 1.2 | 0.5 | 3.6 | 1.4 | 5.6 | 2.1 | 4.1 | 3.3 | 0.3 | 0.1 | 0.9 | 0.4 | 1.1 | 0.5 | ✓ ✗ | ✗ ✗ | Y
I16076_O1i | [-140, -97] | – | 2.1 | – | 10.2 | – | 28.7 | – | 2.2 | – | 1.0 | – | 4.7 | – | 10.9 | – | ✓ – | ✓ – | N
I16076_O1j | [-116, -97] | – | 2.6 | – | 9.1 | – | 17.1 | – | 3.2 | – | 0.8 | – | 2.8 | – | 4.4 | – | ✗ – | ✗ – | N
I16076_O1k | [-136, -97] | – | 0.9 | – | 2.9 | – | 5.6 | – | 1.3 | – | 0.7 | – | 2.2 | – | 3.6 | – | ✗ – | ✗ – | N
I16076_O1l | [-119, -97] | – | 0.2 | – | 0.8 | – | 1.5 | – | 2.3 | – | 0.1 | – | 0.3 | – | 0.6 | – | ✓ – | ✗ – | N
I16076_O2 | – | [ -77, -55] | – | 3.4 | – | 10.8 | – | 18.6 | – | 7.2 | – | 0.5 | – | 1.5 | – | 2.1 |  ✓ | – ✗ | y
I16076_O3a | [-120, -97] | – | 3.2 | – | 9.3 | – | 14.9 | – | 5.2 | – | 0.6 | – | 1.8 | – | 2.4 | – | ✗ – | ✗ – | y
I16076_O4a | – | [ -77, -54] | – | 3.2 | – | 12.2 | – | 25.3 | – | 2.7 | – | 1.2 | – | 4.6 | – | 7.9 | – ✗ | – ✗ | N
I16076_O4b | [-121, -97] | – | 0.4 | – | 1.4 | – | 2.8 | – | 1.3 | – | 0.3 | – | 1.1 | – | 1.8 | – | ✗ – | ✗ – | N
I16076_O5 | [-107, -97] | – | 0.2 | – | 0.7 | – | 1.0 | – | 4.5 | – | 0.1 | – | 0.1 | – | 0.2 | – | ✗ – | ✗ – | y
I16272_O1a | [ -84, -54] | [ -38, 18] | 13.1 | 15.6 | 40.3 | 62.8 | 72.5 | 173.7 | 3.2 | 2.4 | 4.1 | 6.6 | 12.7 | 26.6 | 18.9 | 60.9 | ✓ ✓ | ✓ ✓ | Y
I16272_O1b | [ -63, -54] | [ -38, -27] | 0.8 | 0.5 | 1.7 | 1.1 | 1.9 | 1.3 | 11.1 | 7.0 | 0.1 | 0.1 | 0.2 | 0.2 | 0.1 | 0.2 | ✓ ✓ | ✓ ✓ | Y
I16272_O1c | – | [ -38, -18] | – | 0.2 | – | 0.7 | – | 1.2 | – | 2.4 | – | 0.1 | – | 0.3 | – | 0.4 |  ✓ | – ✗ | y
I16272_O2 | – | [ -38, -2] | – | 3.2 | – | 9.3 | – | 15.9 | – | 2.4 | – | 1.3 | – | 3.8 | – | 5.4 | – ✗ | – ✗ | y
I16351_O1a | [ -58, -45] | [ -35, 48] | 13.7 | 32.3 | 30.7 | 202.6 | 36.8 | 833.8 | 2.7 | 0.6 | 5.1 | 52.6 | 11.5 | 329.7 | 11.4 | 1124.3 | ✓ ✓ | ✓ ✓ | Y
I16351_O1b | [ -72, -45] | – | 1.8 | – | 4.5 | – | 6.8 | – | 4.2 | – | 0.4 | – | 1.1 | – | 1.3 | – | ✗ – | ✗ – | y
I17204_O1a | [ -54, -25] | [ -9, 0] | 2.4 | 0.5 | 7.4 | 1.1 | 12.7 | 1.2 | 1.9 | 4.5 | 1.3 | 0.1 | 3.9 | 0.2 | 5.5 | 0.2 | ✓ ✓ | ✓ ✓ | N
I17204_O1b | [ -46, -25] | – | 0.9 | – | 2.7 | – | 4.8 | – | 3.4 | – | 0.2 | – | 0.8 | – | 1.2 | – | ✓ – | ✓ – | N
I17204_O1c | [ -49, -25] | – | 2.0 | – | 7.3 | – | 14.6 | – | 3.1 | – | 0.6 | – | 2.3 | – | 3.9 | – | ✓ – | ✓ – | N
I17204_O1d | [ -38, -25] | – | 5.7 | – | 14.9 | – | 20.1 | – | 8.6 | – | 0.7 | – | 1.7 | – | 1.9 | – | ✗ – | ✓ – | y
I17220_O1 | [-123, -102] | [ -86, -69] | 2.4 | 3.0 | 7.2 | 8.8 | 12.1 | 14.0 | 4.2 | 4.3 | 0.6 | 0.7 | 1.7 | 2.0 | 2.4 | 2.7 | ✓ ✓ | ✓ ✓ | Y
I17220_O2 | [-140, -102] | [ -86, 0] | 11.6 | 10.2 | 38.5 | 60.7 | 80.1 | 286.0 | 3.5 | 1.7 | 3.3 | 6.0 | 10.8 | 35.7 | 18.7 | 139.2 | ✓ ✓ | ✗ ✗ | Y
I17220_O3 | [-146, -102] | [ -86, -62] | 32.8 | 1.4 | 114.8 | 4.0 | 249.7 | 6.8 | 3.0 | 5.6 | 11.0 | 0.3 | 38.6 | 0.7 | 69.5 | 1.0 | ✓ ✓ | ✓ ✗ | Y
I17220_O4 | [-143, -102] | [ -86, -64] | 15.9 | 2.6 | 58.6 | 6.6 | 135.2 | 9.6 | 2.9 | 2.6 | 5.5 | 1.0 | 20.5 | 2.5 | 39.1 | 3.0 | ✓ ✓ | ✓ ✓ | Y
I17220_O5 | – | [ -86, -67] | – | 0.4 | – | 1.2 | – | 1.9 | – | 1.6 | – | 0.3 | – | 0.7 | – | 1.0 | – ✗ | – ✗ | y
I17220_O6 | – | [ -86, -63] | – | 0.6 | – | 1.9 | – | 3.5 | – | 1.5 | – | 0.4 | – | 1.3 | – | 2.0 | – ✓ | – ✓ | y
a WS imples well-separated outflows without contamination from nearby lobes; Y
and y indicates the bipolar and unipolar outflows, respectively.

###  3.4 Mass of the host cores

Smaller structures within molecular clouds are defined as clumps, cores, and
substructures of a core based on their spatial extent in dust continuum and
line emission maps. In this paper, we adopted the terminology recommended by
Williams et al. (2000) and Wang et al. (2014): clumps have sizes $\sim$1 pc,
dense cores have $\sim$0.1 pc, and substructures within cores have sizes
$\sim$0.01 pc. Small scale structures in 0.9 mm continuum ALMA data were
identified using the Python-based astrodendro-
package222https://dendrograms.readthedocs.io/en/stable/index.html which uses
the dendrogram algorithm to identify the hierarchical structures (i.e., cores)
in the continuum maps. A detailed discussion of the algorithm can be found in
Rosolowsky et al. (2008). The astrodendro-package needs the inputs – the
minimum flux level and the minimum number of pixels – to be considered. A
minimum flux level of 3$\sigma$ was adopted as a good value where $\sigma$ is
the background flux estimated from the emission-free areas in the continuum
map. We considered structures having an area of more than the beam size of 25
pixels (corresponding to a spatial scale of $\sim$0.007–0.02 pc at distances
of 2.6–7.6 kpc) which is sufficient to identify dense cores. A detailed
analysis of the cores will be presented in a future paper. In this paper, we
only present the parameters (i.e., outflow name, coordinates of the continuum
source, size, position angle, and integrated flux) of those 41 cores that are
associated with outflows (see Table 2). These cores have typical sizes in the
range from 0.01–0.06 pc which confirms them to be cores or smaller
substructures within cores. However, from now onward we shall refer to them as
cores.

We further estimated the dust mass of each core assuming the dust emission to
be optically thin using the following equation :

$M_{\rm{dust}}=\frac{F_{\nu}d^{2}}{B_{\nu}(T_{\rm dust})\kappa_{\nu}},$ (9)

where $F_{\nu}$ is the total dust continuum flux of the identified core at
frequency $\nu$, $d$ is the distance to the source, $B_{\nu}(T_{\rm dust})$ is
the Planck function at the temperature $T_{\rm dust}$, and $\kappa_{\nu}$ =
10($\frac{\nu}{1.2THz}$)β cm2 g-1 is the dust opacity (Hildebrand, 1983) where
$\beta$ is the spectral index. As mentioned before the targets here are
massive star-forming regions and are associated with Hii regions. In such an
environment, the cores in the vicinity of Hii regions could be externally
heated to a high temperature of more than 100 K (see e.g., Osorio et al.,
1999; Mookerjea et al., 2007, and references therein). Conversely, in the case
of a cold star-forming core, $T_{\rm dust}$ is typically assumed to be
$\lesssim$30 K (e.g., Plunkett et al., 2013; Wang et al., 2014). Hence, in the
absence of a line-temperature measurement, we safely considered a dust
temperature ($T_{\rm dust}$) of 50 K to calculate masses of the cores in these
regions. The estimated mass of the cores for two dust emissivity spectral
indices (i.e., $\beta=$ 1.5 and 2.0) are listed in Table 2 along with the
other details of the identified cores. The number of outflow lobes associated
with each core is also listed in the last column of Table 2. The mass
estimated using $\beta=$ 2.0 is approximately a factor of 2 higher compared to
the mass estimated using $\beta=$ 1.5.

A histogram for the core mass considering the $T_{\rm dust}$ of 50 K and
$\beta$ of 2, is shown in Figure 4c. The mass of the cores is typically up to
a few tens of solar masses with a couple of sources more than one hundred
solar masses. Note that the mass is estimated here assuming the emission to be
optically thin which might not always hold. There is also a possibility that
multiple cores are located along the line of sight, and together they may
appear as a single core with considerably high mass. Such a possibility cannot
be ignored given the fact that several cores in our sample drive multiple
outflows (see Table 2), indicating the presence of multiple outflow driving
sources within 0.1 pc scale. Hence, estimated core mass with such a large
uncertainty may show a spurious relation when compared with the outflow
parameters. To reduce such possibility, we defined a cleaned set of well-
defined outflows (discussed in Section 4.2). This definition of well-defined
outflows rejects the most massive cores in our sample which hold the highest
possibility of having a companion. Although this precaution helps us to reduce
contamination, we still cannot rule out the possibility of multiplicity in the
identified cores as the regions are located considerably higher distances.
There still might exist unresolved multiples. In an unresolved multiple, a
single core may drive outflows while its companion(s) might remain inactive.
Thus, the estimated core mass should be considered as an upper limit, and any
derived relation or trend of outflow parameters with core masses should be
treated with caution.

Table 2: Details of the driving cores Outflow |  $\alpha$ (J2000) |  $\delta$ (J2000) | Source Size | $F_{\mathrm{int}}$ |  $M_{\mathrm{Core,50K}}$ ($M_{\odot}$) | No. of | Well-Defined
---|---|---|---|---|---|---|---
Name | (h m s) | (${}^{\circ}~{}^{\prime}~{}^{\prime\prime}$ ) | pc$\times$pc, PA (∘) | (mJy) |  $\beta$ = 1.5 |  $\beta$ = 2.0 | lobes | Outflow?
I14382_O1 | 14 42 02.106 | -60 30 44.590 | 0.05$\times$0.03, 89 | 416.7 | 14.0 | 26.2 | 3 | N
I14382_O2 | 14 42 03.066 | -60 30 26.020 | 0.01$\times$0.01, 163 | 34.4 | 1.2 | 2.2 | 2 | Y
I14382_O3 | 14 42 02.833 | -60 30 49.990 | 0.01$\times$0.01, 132 | 3.9 | 0.1 | 0.2 | 1 | N
I14498_O1 | 14 53 42.681 | -59 08 52.880 | 0.02$\times$0.01, 177 | 1034.7 | 21.2 | 39.6 | 2 | Y
I14498_O2 | ** | ** | – | – | – | – | 2 | N
I14498_O3 | 14 53 43.579 | -59 08 43.780 | 0.04$\times$0.02, -135 | 49.0 | 1.0 | 1.9 | 1 | Y
I14498_O4 | 14 53 42.941 | -59 09 00.870 | 0.03$\times$0.02, 87 | 164.6 | 3.4 | 6.3 | 1 | Y
I15520_O1 | 15 55 48.398 | -52 43 06.530 | 0.01$\times$0.00, -158 | 133.1 | 1.8 | 3.4 | 2 | Y
I15520_O2 | 15 55 48.654 | -52 43 08.660 | 0.01$\times$0.01, 172 | 433.4 | 5.9 | 10.9 | 2 | N
I15520_O3 | 15 55 48.393 | -52 43 04.380 | 0.02$\times$0.01, 76 | 1102.8 | 14.9 | 27.8 | 1 | N
I15520_O4 | 15 55 48.848 | -52 43 01.610 | 0.01$\times$0.00, 98 | 22.9 | 0.3 | 0.6 | 1 | N
I15520_O5 | 15 55 49.265 | -52 43 03.020 | 0.01$\times$0.01, -146 | 21.2 | 0.3 | 0.5 | 1 | N
I15596_O1 | 16 03 31.921 | -53 09 22.960 | 0.01$\times$0.01, 47 | 63.3 | 2.5 | 4.6 | 4 | N
I15596_O2 | 16 03 32.646 | -53 09 26.820 | 0.02$\times$0.01, 156 | 108.5 | 4.2 | 7.8 | 4 | N
I15596_O3 | 16 03 32.656 | -53 09 45.760 | 0.03$\times$0.01, 70 | 12.4 | 0.5 | 0.9 | 1 | Y
I15596_O4 | 16 03 32.705 | -53 09 29.570 | 0.02$\times$0.02, 63 | 86.1 | 3.3 | 6.2 | 2 | Y
I15596_O5 | 16 03 31.697 | -53 09 32.090 | 0.03$\times$0.02, -176 | 253.7 | 9.8 | 18.3 | 2 | Y
I15596_O6 | ** | ** | – | – | – | – | 2 | Y
I15596_O7 | 16 03 32.927 | -53 09 27.850 | 0.01$\times$0.01, -155 | 3.8 | 0.1 | 0.3 | 1 | Y
I15596_O8 | 16 03 30.635 | -53 09 33.990 | 0.02$\times$0.01, 159 | 28.9 | 1.1 | 2.1 | 1 | Y
I16060_O1 | 16 09 52.650 | -51 54 54.860 | 0.01$\times$0.01, 165 | 1205.3 | 65.3 | 121.7 | 2 | N
I16060_O2 | 16 09 52.450 | -51 54 55.790 | 0.02$\times$0.02, 72 | 3157.7 | 171.0 | 318.9 | 1 | N
I16060_O3 | ** | ** | – | – | – | – | 1 | Y
I16060_O4 | 16 09 52.803 | -51 54 57.900 | 0.01$\times$0.01, -170 | 143.1 | 7.8 | 14.4 | 1 | Y
I16071_O1 | 16 10 59.750 | -51 50 23.540 | 0.06$\times$0.04, 106 | 7058.5 | 339.3 | 632.9 | 8 | N
I16071_O2 | 16 10 59.553 | -51 50 27.510 | 0.02$\times$0.01, 53 | 53.7 | 2.6 | 4.8 | 2 | Y
I16071_O3 | 16 10 59.400 | -51 50 16.520 | 0.03$\times$0.02, 160 | 98.2 | 4.7 | 8.8 | 2 | Y
I16071_O4 | 16 11 00.242 | -51 50 26.220 | 0.01$\times$0.01, -170 | 5.2 | 0.2 | 0.5 | 1 | Y
I16071_O5 | 16 10 58.732 | -51 50 36.370 | 0.04$\times$0.01, 175 | 29.2 | 1.4 | 2.6 | 1 | Y
I16071_O6 | 16 10 59.286 | -51 50 11.780 | 0.01$\times$0.01, 71 | 5.6 | 0.3 | 0.5 | 4 | N
I16071_O7 | 16 10 58.742 | -51 50 17.330 | 0.02$\times$0.01, 151 | 10.0 | 0.5 | 0.9 | 1 | Y
I16076_O1 | 16 11 26.540 | -51 41 57.320 | 0.04$\times$0.02, 101 | 858.8 | 43.0 | 80.2 | 17 | N
I16076_O2 | 16 11 27.384 | -51 41 50.210 | 0.02$\times$0.01, 50 | 10.2 | 0.5 | 0.9 | 1 | Y
I16076_O3 | 16 11 27.697 | -51 41 55.360 | 0.05$\times$0.02, 153 | 255.7 | 12.8 | 23.9 | 1 | Y
I16076_O4 | 16 11 26.876 | -51 41 55.920 | 0.01$\times$0.01, 122 | 16.4 | 0.8 | 1.5 | 1 | N
I16076_O5 | ** | ** | – | – | – | – | 1 | Y
I16272_O1 | 16 30 58.770 | -48 43 53.890 | 0.01$\times$0.01, -168 | 2288.4 | 46.9 | 87.5 | 5 | N
I16272_O2 | ** | ** | – | – | – | – | 1 | Y
I16351_O1 | 16 38 50.501 | -47 28 00.910 | 0.02$\times$0.01, 109 | 2000.8 | 33.7 | 62.8 | 3 | N
I17204_O1 | 17 23 50.249 | -36 38 59.660 | 0.03$\times$0.02, 148 | 698.2 | 11.8 | 21.9 | 5 | N
I17220_O1 | 17 25 25.635 | -36 12 35.120 | 0.04$\times$0.03, -144 | 175.0 | 20.2 | 37.7 | 2 | Y
I17220_O2 | 17 25 24.796 | -36 12 36.850 | 0.03$\times$0.01, 64 | 12.7 | 1.5 | 2.7 | 2 | Y
I17220_O3 | 17 25 24.357 | -36 12 47.890 | 0.03$\times$0.01, 160 | 20.3 | 2.4 | 4.4 | 2 | Y
I17220_O4 | 17 25 24.453 | -36 12 39.360 | 0.03$\times$0.02, -147 | 47.8 | 5.5 | 10.3 | 2 | Y
I17220_O5 | 17 25 24.926 | -36 12 43.440 | 0.02$\times$0.01, 160 | 29.8 | 3.4 | 6.4 | 1 | Y
I17220_O6 | 17 25 25.697 | -36 12 39.480 | 0.06$\times$0.03, 178 | 170.9 | 19.8 | 36.9 | 1 | Y
$**$ The outflows without any detected continuum source |  |

##  4 Discussion

###  4.1 Comparison among outflow parameters

The regions studied here have shown a complex outflow morphology. Thus,
estimated outflow parameters might be contamineted by the pixels of the nearby
outflow lobes particularly toward the lower velocity ends. Thus, to separate
out the contaminated lobes, we examined each region by overlaying the areas
used to calculate the outflow parameters for all the identified lobes.
Accordingly, we defined a cleaned set of well-separated outflow lobes which do
not have any overlapping pixel (in the PP space) with a neighbouring lobe. The
final column of Table 1 denotes whether the outflow lobe is well-separated or
not. In Figure 5a, we have plotted $M_{\rm out}$ versus $\dot{M}_{\rm out}$
for all the lobes. The well-separated lobes are also marked in the figure. A
nice correlation is noted between $\dot{M}_{\rm out}$ and $M_{\rm out}$
(Figure 5a) which is possibly expected as a higher gas outflow rate should
inject more matter into the surrounding environments. Spearman’s rank
correlation coefficients ($\rho$) are also marked in the figure for both the
sets of data. We further plotted the $M_{\rm out}$ with respect to the $t_{\rm
dyn}$ of all the lobes (Figure 5b). A non-correlation ($\rho$ = 0.07 for all
and $\rho$ = -0.08 for all well-separated lobes) can be noted among these two
parameters. A similar non-correlation trend was noted previously by Wu et al.
(2004) for the high-mass clumps. However, they noted an increasing trend of
the outflow mass with time for the group of low-mass clumps. This is
particularly puzzling for our sample because most of the outflows in this
study are low-mass outflows. For further confirmation of this non-correlation,
we performed a Student’s $t$-test for the significance level of the null
hypothesis following the similar method described in Li et al. (2003). We
found that the test produces $p$-value greater than 0.9 which rejects the null
hypothesis. This particular test indicates that the observed non-correlation
might be a random result. A possible reason for this could be the
comparatively smaller range of dynamical time-scales ($\sim$0.3–11$\times$103
yr) in our sample compared to that reported by Wu et al. (2004,
$\sim$1–550$\times$103 yr).

We further examined the outflow rate against the dynamical time-scale. An
anti-correlation trend ($\rho$ = -0.34 and -0.42 for all and well-separated
lobes, respectively) is noted for $\dot{M}_{\rm out}$ with $t_{\rm dyn}$
(Figure 5c). This observed trend could be due to a decrease in the outflow
rate with time as also previously suggested by several authors (Motte et al.,
2007; López-Sepulcre et al., 2011; Watson et al., 2016, and references
therein).

Figure 5d shows the relation between the maximum dynamical time-scale ($t_{\rm
dyn,max}$) of the outflows associated with a single core and the core mass. A
hint of positive correlation is found for $t_{\rm dyn,max}$ with the core mass
(Spearman’s Rank correlation coefficient $\rho$ = 0.35 and 0.16 for all and
well-separated lobes, respectively). A positive trend was o observed in young
infrared dark clouds by Li et al. (2020). This positive trend might indicate
for the massive cores to have comparatively longer outflow dynamical timescale
than their low-mass counterparts. If it is generally assumed that the
dynamical time-scale of outflows reflects the accretion time-scale of the
cores, then this positive trend might also hint at the possibility for the
massive cores to have longer accretion history than the low mass cores in
these protoclusters as also suggested by Li et al. (2020). However, as already
discussed before a few cores in our sample might not be well-resolved. The
presence of an unresolved companion would overestimate the core mass. Thus,
this observed increasing trend might vary depending on an improved core mass
estimation. For a more robust explanation, it needs a comparison of $t_{\rm
dyn}$ with the bolometric luminosity of the associated cores. However, the
determination of bolometric luminosity requires multi-band observations of the
source at a similar spatial resolution. No other observations are available
for our targets at the angular resolution of ALMA, and it is thus difficult to
determine the bolometric luminosity of the cores in our sample.

Figure 5: (a) Outflow mass versus the outflow rate for each lobe, (b) outflow
mass against the dynamical time-scale of the outflows, (c) outflow rate
against the dynamical time-scale of the outflows, and (d) the dynamical time-
scale versus the mass of their host cores. The data points marked with green
circles/square are for well-separated lobes. The black dashed lines in all the
panels are the linear least-square fits on a logarithmic scale to all data
points while the green dashed lines show the linear least-square fit to the
well-separated data points in logarithminc scale. In each panel, the
corresponing Spearman’s Rank correlation coefficients ($\rho$) are also
written in black and green, respectively.

###  4.2 Comparison between outflow parameters and core parameters

Figure 6a shows the outflow mass plotted as a function of core mass, $M_{\rm
core}$. Note that several massive cores are found associated with multiple
outflows (both bipolar and unipolar). These outflows may be driven by further
small-scale cores or multiple cores along the line of sight. However, such
individual driving cores/sub-cores are not resolved even at the resolution of
our ALMA data, possibly because our targets are densely clustered environment
located at comparatively large distances (2.6–7.6 kpc). Note that the presence
of several outflows has been noted before in other massive star-forming
regions (see e.g., Beuther et al., 2002; Zhang et al., 2007; Fernández-López
et al., 2013; Nony et al., 2020). In such a dense clustered environment, it is
difficult to always identify bipolar signatures of outflows. For uniformity,
we considered plotting the total outflow parameters (see triangles in Figure
6) driven by each identified core as that helps us to have a better comparison
between the outflow and core properties. Additionally, for clarity we defined
a cleaned well-defined set of outflow and their host cores. In Section 4.1, we
already defined a set of well-separated lobes. Here, we further examine the
well-separated outflows with respect to their host cores, and rejected those
for which a host core is associated with more than one outflow (could be
bipolar or unipolar). Finally, we named this cleaned set of data as well-
defined outflows. We also marked these well-defined outflows in Figure 6 (see
green triangles). Note that our calculated parameters are based on the
emission detected in interferometric data which might lose the extended
emission flux from the target. There are other additional components (i.e.,
assumption of optically thin emission, projection effect of outflow lobes,
overlapping outflows) which underestimate the calculated parameters (see
Section 3.3). Similarly, values of the cores might have a large uncertainty
depending on the adopted dust temperature and the spectral index in the
calculation, missing extended emission, and presence of unresolved companions
(see Section 3.4).

A correlation trend ($\rho$ = 0.48) is found between the outflow mass and the
core mass for all the outflows (Figure 6a). Even though an increasing trend is
noted, a low correlation coefficient ($\rho$ = 0.20) is obtained when only
well-defined outflows are considered. The general increasing trend fits to a
power-law of $M_{\rm out}\propto M^{0.5}_{\rm core}~{}and~{}M^{0.4}_{\rm
core}$ for all and well-defined cases, respectively. A similar proportionality
is seen at clump-scale by Yang et al. (2018, $M_{\rm out}\propto M^{0.6}_{\rm
clump}$) for massive outflows associated with hundreds of ATLASGAL clumps.
However, a few other studies found a slightly steeper relation between outflow
mass and the core mass (i.e., $M_{\rm out}\propto M^{0.8}_{\rm core};$ Beuther
et al., 2002; López-Sepulcre et al., 2009; de Villiers et al., 2014). Most of
the previous studies were based on a few tens of outflows. Thus, the slightly
shallower slope in our sample could be a result of a large range of core mass
as also pointed out by Yang et al. (2018). The ratio $M_{\rm out}/M_{\rm
core}$ has an average value of 0.05$\pm$0.13 which implies that on average 5%
of the core gas is entrained in the molecular outflow. This entrainment ratio
is comparable to the value of 4–6% reported by Beuther et al. (2002), Yang et
al. (2018), and Li et al. (2018) seen at the clump-scale analysis.

Figure 6b shows $\dot{M}_{\rm out}$ plotted as a function of $M_{\rm core}$.
Despite a large scatter in the data points, a positive trend ($\rho$ = 0.42
for all outflows and $\rho$ = 0.24 for well-defined outflows) can be noted for
the wide-range of $M_{\rm core}$ values. A similar positive trend with a
similar slope was noted in previous studies toward massive clump-scale
outflows (see López-Sepulcre et al., 2010; Yang et al., 2018; Li et al., 2018,
and references therein). In addition, we also plotted outflow energy ($E_{\rm
out}$) and mechanical luminosity of the outflow ($L_{\rm mech}$) with respect
to the core mass, $M_{\rm core}$ (see Figure 6c,d). A positive trend is noted
for $E_{\rm out}$ with $M_{\rm core}$ for all outflows. However, the trend is
not significant when only well-defined set of outflows are considered.
Similarly, the positive trend for $L_{\rm mech}$ with $M_{\rm core}$ is not
significant, specifically for the well-defined outflows. The correlation here
is not statistically significant for both well-defined and all outflows (i.e.
weak or non-correlation). Overall, these results suggest that the energy of an
outflow generally increases with increasing mass of the driving core, but
without a tight correlation.

Figure 6: (a) The outflow mass versus the core mass, (b) the outflow rate
versus the core mass, (c) the outflow energy versus the core mass, and (d) the
mechanical luminosity of the outflow versus the core mass. The triangles
represent the total outflow parameters associated with each core. The green
triangles represent the well-defined outflows (see text). The green triangles
with black edges correspond to the unipolar outflows while the remaining green
triangles are for bipolar outflows. The red triangles are the sources that are
contaminated either by driving multiple outflow lobes or the lobe has
overlapping emission with a nearby lobe. The blue and green dashed lines are
the linear least-square fit to all and green data points in the logarithmic
scale, respectively. The Spearman’s rank correlation coefficients ($\rho$ in
green for well-defined and in blue for all outflows) are also marked in all
the panels.

The mechanical force of an outflow, $F_{\rm out}$, can be considered as a
measure of the strength of the outflow, and the rate at which the outflow
injects momentum (at the current epoch) into the surrounding gas (Bachiller &
Gomez-Gonzalez, 1992; Downes & Cabrit, 2007). Several studies have already
reported a positive correlation of $F_{\rm out}$ with core/clump mass and also
with the bolometric luminosities (see Shepherd & Churchwell, 1996; Wu et al.,
2004; Zhang et al., 2005; Yang et al., 2018, for example). Our calculated
$F_{\rm out}$ (mainly composed of low-mass outflows) is plotted against
$M_{\rm core}$ in Figure 7a. An increasing trend ($\rho$ = 0.39 and 0.21 for
all and well-defined outflows, respectively) is noted for $F_{\rm out}$
against core mass over 3 orders of magnitude. A linear fit to the data points
shows a slope of 0.5 for all outflows and 0.4 for the well-defined outflows.

To examine the overall clump-scale scenario, we plotted the total value of
$F_{\rm out}$ for each region against the total mass of the clump (obtained
from Liu et al., 2020a) along with the parameters for more than one hundred of
ATLASGAL clumps studied by Yang et al. (2018) (see Figure 7b). In the absence
of bolometric luminosity of individual cores, we also plotted the total
$F_{\rm out}$ of each target against the bolometric luminosity of the whole
clump (see Urquhart et al., 2018; Liu et al., 2020a, for detailed parameters)
along with parameters from Yang et al. (2018) (see Figure 7c). As can be seen
in Figure 7b,c, our sample typically follows a similar increasing trend that
was seen by Yang et al. (2018). The data points for our eleven targets roughly
fall within the parametric distribution of Yang et al. (2018).

A hint of increasing trend for $F_{\rm out}$ with $M_{\rm core}$ that holds
for 3 orders of magnitude of core mass indicates a possible similar mechanism
for driving outflows over the whole range of low-mass to massive cores.
However, a slightly different slope is obtained at core-scale (slope=0.5 and
0.4, Figure 7a) compared to clump-scale (slope=0.8, Figure 7b). A similar
increasing trend was noted before for low-mass and massive clumps by Maud et
al. (2015) and Yang et al. (2018) for $F_{\rm out}$ against bolometric
luminosity. These authors, however, did not exclude the possibility of
observational bias between the outflow force of low-luminosity and high-
luminosity sources considering that they lie at different distances. The
targets of this work are massive protoclusters where we primarily detected
low-mass outflows along with a few high-mass outflows. We thus conclude that
the increasing trend is possibly real as both low-mass and high-mass outflows
in our study are identified in the same target region.

Considering the overall trend of the outflow parameters with the parametrs of
the cores, a general inclination might be to believe that the stars of all
different masses produce ouflows by a similar mechanism. However, as also
discussed by Maud et al. (2015), the outflow parameters such as $\dot{M}_{\rm
out}$ and $F_{\rm out}$ have an intrinsic dependency on the entrained mass by
the outflows. Thus, these parameters can only infer the strength of the
outflows, not the launching mechanism. The outflow launching mechanisms for
low-mass and massive star formation could thus be substantially different.

The mass accretion rate could provide us the information on the ongoing star
formation activity in the host cores. In general, an accretion rate of
$\lesssim$10-5 M⊙ yr-1 is typical for low-mass star formation, whereas a much
higher accretion rates of $\gtrsim$10-4 M⊙ yr-1 is required for massive star
formation (McKee & Tan, 2003). It is possible to get an estimate of the
accretion rate from the derived outflow mechanical force if it assumed that
(1) the observed outflows are driven by proto-stellar winds from accretion
disks (Keto, 2003), (2) the momentum is conserved between the wind and the
outflow, and (3) the wind and molecular gas interface is efficiently mixed
(Richer et al., 2000). Then, the accretion rate can be formulated as
$\dot{M}_{\rm acc}$ = $k\dot{M}_{\rm wind}$, where $\dot{M}_{\rm wind}$ is the
mass-loss rate of the wind which can be inferred from the $F_{\rm out}$ (=
$\dot{M}_{\rm wind}v_{\rm wind}$). Here, $v_{\rm wind}$ is the wind velocity
which we adopted to be 500 km s-1 (Bally, 2016; Li et al., 2020) and $k$ is
the ratio between the mass accretion rate and the mass ejection rate which we
assumed to be 3 (Shu et al., 2000). Accordingly, the derived accretion rates
range from 4.6$\times$10-8 to 3.0$\times$10-4 M⊙ yr-1, with an average value
of 2.4$\times$10-5 M⊙ yr-1. Although the mass accretion rates derived here are
typically small compared to the expected accretion rate (10-4–10-3 M⊙ yr-1)
for high-mass star formation (McKee & Tan, 2003; Wang et al., 2010), a couple
of sources also show comparable accretion rates that are seen toward high-mass
star-forming regions (Zhang et al., 2005; Qiu et al., 2009; Liu et al., 2016,
2017).

These typically low accretion rates of $\lesssim$10-5 M⊙ yr-1 imply that the
majority of the outflows in our studied regions are associated with low-mass
star formation (see e.g., Machida & Hosokawa, 2013; Frank et al., 2014; Lee,
2020). In the case of low-mass star formation, it is observed that the
accretion rate falls off with time at an approximate $t^{-1}$ law (Caratti o
Garatti et al., 2012). Additionally, the mass accretion rates for low-mass
stars depends on the mass (M∗) of the forming star (see Alcalá et al., 2014;
Biazzo et al., 2019, and references therein). While the determination of the
stellar mass and the evolutionary stages of the observed cores is beyond the
scope of this paper, we also note that uncertainties in mass accretion rates
are large, resulting from the uncertainties of outflow parameters and adopted
assumptions. Although this calculation prone to large uncertainty, it may
still provide us the information on the general star formation scenario in
these studied regions. We found that currently these regions are mostly going
through an active phase of low-mass star formation.

Figure 7: (a) Outflow force plotted against the mass of the associated cores.
The triangles represent the total outflow parameters associated with each
core. The green triangles represent the well-defined outflows whereas the red
triangles are contaminated lobes (see text and caption of Figure 6). The blue
and green dashed lines are the linear least-square fit to all and green data
points in the logarithmic scale, respectively. (b) Total outflow force plotted
against the mass of the whole clump. (c) Total outflow force plotted against
the bolometric luminosity of the clump. Red squares in panels (b) and (c) show
the data points for our targets while the black diamonds are obtained from
Yang et al. (2018). Blue dashed lines in both the panels represent the linear
least-square fits on a logarithmic scale. The equation of the best fit lines
are also labelled and correlation coefficients are also indicated in each
panel.

Figure 8: (a) The ratios $E_{\rm grav}/E_{\rm turb}$ (black squares) and $E_{\rm outflow}/E_{\rm turb}$ (red diamonds) plotted as a function of the turbulent energy. The dashed black line represents the ratio of one. (b) The comparison between turbulent dissipation rate and the mechanical energy injection rate. The blue dashed line represents equal rates of turbulent dissipation and mechanical energy injection, and the grey-shaded area shows a variation of one order of magnitude around the blue dashed line.  Table 3: Energy budget of the observed protoclusters Source | Rada | Log($L_{\rm bol})^{a}$ | Log($M_{\rm clump})^{a}$ | $\Delta v({\rm C^{17}O})^{b}$ | $E_{\rm grav}$ | $E_{\rm turb}$ | $E_{\rm outflow}$ | $t_{\rm diss}$ | $L_{\rm turb}$ | $L_{\rm Mech}$
---|---|---|---|---|---|---|---|---|---|---
| (pc) | ($L_{\odot}$) | ($M_{\odot}$) | (km s-1) | ($\times$10${}^{38}~{}J$) | ($\times$105 yr) | ($\times$10${}^{26}~{}J$ s-1)
I14382-6017 | 1.68 | 5.2 | 3.6 | 3.03 | 484 | 197 | 1 | 6.4 | 10 | 17
I14498-5856 | 0.74 | 4.4 | 3.0 | 3.37 | 69 | 61 | 3 | 2.5 | 8 | 79
I15520-5234 | 0.67 | 5.1 | 3.2 | 4.53 | 192 | 175 | 2 | 1.7 | 33 | 19
I15596-5301 | 1.81 | 5.5 | 3.9 | 3.87 | 1789 | 640 | 13 | 5.4 | 38 | 373
I16060-5146 | 1.24 | 5.8 | 3.9 | 5.02 | 2612 | 1077 | 18 | 2.8 | 120 | 104
I16071-5142 | 1.21 | 4.8 | 3.7 | 5.80 | 1066 | 907 | 56 | 2.4 | 120 | 2286
I16076-5134 | 1.57 | 5.3 | 3.6 | 4.89 | 518 | 512 | 9 | 3.7 | 44 | 230
I16272-4837 | 0.84 | 4.3 | 3.2 | 3.66 | 154 | 114 | 4 | 2.6 | 14 | 48
I16351-4722 | 0.69 | 4.9 | 3.2 | 4.59 | 187 | 180 | 13 | 1.7 | 33 | 638
I17204-3636 | 0.60 | 4.2 | 2.9 | 3.11 | 54 | 41 | 1 | 2.2 | 6 | 7
I17220-3609 | 2.41 | 5.7 | 4.3 | 4.53 | 8479 | 2203 | 12 | 6.1 | 114 | 156
a Values from Urquhart et al. (2018) |  |  |  |  |  |
b Values from Yue et al. (private communication) |  |  |  |  |  |

###  4.3 Energy budgets of the clumps and dissipation rate

It is important to examine the cumulative impact of the outflows on the
dynamics of their host clouds. We thus investigated whether the identified
outflows have sufficient energy to drive the turbulence that counteracts the
gravitational collapse of the host cloud. Estimation of the gravitational
binding energy ($E_{\rm grav}$ = $3GM_{\rm cloud}^{2}/5R_{\rm cloud}$, where
$G$ is gravitational constant) requires the total mass and the radius of the
clouds. These values were obtained from Urquhart et al. (2018) (also see Table
A1 of Liu et al. (2020a)). The mass, radius, and the calculated values of
$E_{\rm grav}$ are listed in Table 3. The turbulent energy of a molecular
cloud is given by

$E_{\rm turb}=\frac{1}{2}M_{\rm cloud}\sigma_{\rm 3d}^{2},$ (10)

where $\sigma_{\rm 3d}$ is the three-dimensional turbulent velocity
dispersion, which can be calculated using

$\sigma_{\rm 3d}=\frac{\sqrt{3}}{2\sqrt{2~{}ln~{}2}}\Delta v_{\rm FWHM},$ (11)

where $\Delta v_{\rm FWHM}$ is the one-dimensional velocity width of an
optically thin tracer of the molecular cloud. We obtained the $\Delta v_{\rm
FWHM}$ values for all our regions from Yue et al. (private communication).
They performed the C17O (3–2) line observations of these sources using the
Atacama Pathfinder Experiment (APEX) and obtained the $\Delta v_{\rm FWHM}$
values by performing a Gaussian fit to the observed line profile. The values
of $\Delta v_{\rm FWHM}$ (i.e., $\Delta v({\rm C^{17}O})$) and the calculated
$E_{\rm turb}$ are listed in Table 3. The table also lists the total energy of
all the outflows ($E_{\rm outflow}$) identified in each region. In Figure 8a
we have shown the ratios of $E_{\rm grav}/E_{\rm turb}$ and $E_{\rm
outflow}/E_{\rm turb}$ versus the turbulent energy. As can be seen in the
figure, although $E_{\rm grav}$ is generally comparable to $E_{\rm turb}$, the
total outflow energy, $E_{\rm outflow}$, is typically 1-3 orders of magnitude
smaller than the turbulent energies in these regions, and thus, cannot drive
the turbulence in these protoclusters. A similar conclusion was reached for
outflows in the Taurus molecular cloud by Li et al. (2015). However, the
regions studied here are massive protoclusters and are mostly associated with
the H ii regions. Thus, a significant amount of turbulence might be
contributed by the massive stars and the associated H ii region. Note that the
origin of such turbulence in the molecular clouds is unclear (see Larson,
1981; Heyer & Brunt, 2004). Hennebelle & Falgarone (2012) suggested that such
turbulence could be driven by external sources, such as, supernovae explosion,
large-scale Hi streams (Ballesteros-Paredes et al., 1999), shocks (McKee &
Ostriker, 2007), or galactic differential rotation (Klessen & Hennebelle,
2010; Hennebelle & Falgarone, 2012).

We further estimated the total outflow energy injection rate (i.e., $L_{\rm
Mech}$) and compare it with the energy rate needed to maintain the turbulence
of the clump (i.e., $L_{\rm turb}$). The turbulent dissipation rate can be
calculated as

$L_{\rm turb}=\frac{E_{\rm turb}}{t_{\rm diss}},$ (12)

where $t_{\rm diss}$ is the turbulent dissipation time. The turbulent
dissipation time is calculated using the equation based on numerical
simulations (McKee & Ostriker, 2007).

$t_{\rm diss}\sim 0.5\frac{D_{\rm cloud}}{\sigma_{\rm 1d}},$ (13)

where $D_{\rm cloud}$ is the diameter of the clump and $\sigma_{\rm 1d}$ is
the one-dimensional turbulent velocity dispersion along the line of sight
(i.e., $\sigma_{\rm 1d}=\frac{\sigma_{\rm 3d}}{\sqrt{3}}$).

The calculated turbulence dissipation time, the turbulence energy dissipation
rate, and the total outflow energy rate for all the targets in our sample are
listed in Table 3. Note that these values should be treated only as order-of-
magnitude estimates because there are several uncertainties involved in the
estimated outflow parameters as already discussed in Section 3.3. In Figure
8b, the turbulent energy dissipation rate, $L_{\rm turb}$, is plotted with
respect to the total outflow energy injection rate, $L_{\rm Mech}$, for our
eleven protoclusters. It can be seen in the figure that the $L_{\rm turb}$ is
typically comparable within an order of magnitude of the $L_{\rm Mech}$ values
for all the clumps. Thus, it seems that the outflow observed here cannot
account for the generation of turbulence, but can sustain the turbulence at
the current epoch as it is similar to the estimated dissipation rate, similar
to what was found in low mass star forming regions (e.g., Li et al., 2015).

##  5 Conclusions

We studied the properties of the outflows toward 11 Galactic massive
protoclusters. Outflows are identified using three different tracers, the CO
(3–2), HCN (4–3), and HCO+ (4–3) lines. A total of 106 outflow lobes have been
detected in these regions as already reported in Paper I. About 40% of these
outflows are identified with a single lobe, possibly because of the dense
clustering nature of the massive protoclusters. All the physical parameters
studied here are derived from CO. The main findings of this study are the
following.

$\bullet$ Although the position angles of outflow lobes do not differ in the
three tracers, HCN and HCO+ generally detect outflows at lower terminal
velocities compared to CO. This is possibly because CO is more sensitive to
detect the low density and high-velocity outflowing material compared to the
HCN and HCO+.

$\bullet$ Identified outflows are young (typical $t_{\rm dyn}~{}\sim$ 102–104
yr) and are composed of low-mass to (a few) massive outflows (e.g.,
$\dot{M}_{\rm out}\sim$ 10-7–10${}^{-4}M_{\odot}$ yr-1; $P_{\rm out}\sim$
10-3–10${}^{+1}M_{\odot}$ km s-1).

$\bullet$ An anti-correlation trend is noted for $\dot{M}_{\rm out}$ versus
$t_{\rm dyn}$. This particular result may indicate that the outflow rate
decreases with time. Although not significant, increase of $t_{\rm dyn}$ with
the mass of the associated core probably indicates that the massive cores
might have longer accretion history than the low mass cores in these
protoclusters.

$\bullet$ The outflow mass for our sample shows a trend that increase with
core mass with a power-law index of $\sim$0.4-0.5. This index is in agreement
with clump-scale massive outflows reported by Yang et al. (2018), but
shallower (power index $\sim$0.9) than found in a few previous findings
(Beuther et al., 2002; López-Sepulcre et al., 2009; de Villiers et al., 2014;
Li et al., 2018). A similar rising trendency is seen for $F_{\rm out}$ with
respect to core mass which could be a result of increasing entrained mass with
increasing mass of the driving source, rather than the outflow launching
mechanism.

$\bullet$ Calculation of the energy budget reveals that the kinetic energy of
outflows alone cannot balance the gravitational binding energy, and is also
unable to generate the observed turbulence in the host molecular clouds of our
target regions. However, the energy injection rates of outflows in most of the
regions can sustain the turbulence at the current epoch.

## Acknowledgements

We thank an anonymous referee for the thorough review and critical comments
that helped us to improve the scientific content and the presentation of the
manuscript. The work is supported by National Science Foundation of China
(11988101, 11721303, 11973013, U1631102, 11373010). TB and KW are supported by
the National Key Research and Development Program of China (2017YFA0402702,
2019YFA0405100). KW also acknowledges the support from a starting grant at the
Kavli Institute for Astronomy and Astrophysics, Peking University
(7101502016). TB acknowledges funding from the China Postdoctoral Science
Foundation through grant 2018M631241 and the PKU-Tokyo Partner fund. TB also
acknowledge the support from S. N. Bose National Centre for Basic Sciences
under the Department of Science and Technology (DST), Govt. of India. This
work was carried out in part at the Jet Propulsion Laboratory, operated for
NASA by the California Institute of Technology. L.B. acknowledges support from
CONICYT project Basal AFB-170002. C.W.L. is supported by the Basic Science
Research Program through the National Research Foundation of Korea (NRF)
funded by the Ministry of Education, Science and Technology
(NRF-2019R1A2C1010851). This research made use of Astropy, a community-
developed core Python package for astronomy (Astropy Collaboration et al.,
2018). This paper makes use of the following ALMA data:
ADS/JAO.ALMA#2017.1.00545.S. ALMA is a partnership of ESO (representing its
member states), NSF (USA) and NINS (Japan), together with NRC (Canada), MOST
and ASIAA (Taiwan), and KASI (Republic of Korea), in cooperation with the
Republic of Chile. The Joint ALMA Observatory is operated by ESO, AUI/NRAO,
and NAOJ.

## Data Availability

The raw data that support this study are publicly available for download from
the ALMA archive. The reduced data are available from the corresponding author
upon reasonable request.

## References

  * Alcalá et al. (2014) Alcalá J. M., et al., 2014, A&A, 561, A2
  * Arce & Goodman (2001) Arce H. G., Goodman A. A., 2001, ApJ, 554, 132
  * Arce et al. (2007) Arce H. G., Shepherd D., Gueth F., Lee C. F., Bachiller R., Rosen A., Beuther H., 2007, in Reipurth B., Jewitt D., Keil K., eds, Protostars and Planets V. p. 245 (arXiv:astro-ph/0603071)
  * Arce et al. (2010) Arce H. G., Borkin M. A., Goodman A. A., Pineda J. E., Halle M. W., 2010, ApJ, 715, 1170
  * Arce et al. (2013) Arce H. G., Mardones D., Corder S. A., Garay G., Noriega-Crespo A., Raga A. C., 2013, ApJ, 774, 39
  * Astropy Collaboration et al. (2018) Astropy Collaboration et al., 2018, AJ, 156, 123
  * Bachiller (1996) Bachiller R., 1996, ARA&A, 34, 111
  * Bachiller & Gomez-Gonzalez (1992) Bachiller R., Gomez-Gonzalez J., 1992, A&ARv, 3, 257
  * Ballesteros-Paredes et al. (1999) Ballesteros-Paredes J., Hartmann L., Vázquez-Semadeni E., 1999, ApJ, 527, 285
  * Bally (2016) Bally J., 2016, ARA&A, 54, 491
  * Baug et al. (2020) Baug T., et al., 2020, ApJ, 890, 44
  * Beuther et al. (2002) Beuther H., Schilke P., Sridharan T. K., Menten K. M., Walmsley C. M., Wyrowski F., 2002, A&A, 383, 892
  * Biazzo et al. (2019) Biazzo K., Beccari G., De Marchi G., Panagia N., 2019, ApJ, 875, 51
  * Blake et al. (1987) Blake G. A., Sutton E. C., Masson C. R., Phillips T. G., 1987, ApJ, 315, 621
  * Bonnell et al. (2001) Bonnell I. A., Bate M. R., Clarke C. J., Pringle J. E., 2001, MNRAS, 323, 785
  * Bontemps et al. (1996) Bontemps S., Andre P., Terebey S., Cabrit S., 1996, A&A, 311, 858
  * Caratti o Garatti et al. (2012) Caratti o Garatti A., et al., 2012, A&A, 538, A64
  * Caratti o Garatti et al. (2015) Caratti o Garatti A., Stecklum B., Linz H., Garcia Lopez R., Sanna A., 2015, A&A, 573, A82
  * Carroll et al. (2009) Carroll J. J., Frank A., Blackman E. G., Cunningham A. J., Quillen A. C., 2009, ApJ, 695, 1376
  * Downes & Cabrit (2007) Downes T. P., Cabrit S., 2007, A&A, 471, 873
  * Dunham et al. (2014) Dunham M. M., Arce H. G., Mardones D., Lee J.-E., Matthews B. C., Stutz A. M., Williams J. P., 2014, ApJ, 783, 29
  * Feddersen et al. (2020) Feddersen J. R., et al., 2020, ApJ, 896, 11
  * Fernández-López et al. (2013) Fernández-López M., Girart J. M., Curiel S., Zapata L. A., Fonfría J. P., Qiu K., 2013, ApJ, 778, 72
  * Franco (1983) Franco J., 1983, ApJ, 264, 508
  * Frank et al. (2014) Frank A., et al., 2014, in Beuther H., Klessen R. S., Dullemond C. P., Henning T., eds, Protostars and Planets VI. p. 451 (arXiv:1402.3553), doi:10.2458/azu_uapress_9780816531240-ch020
  * Green et al. (2011) Green J. D., et al., 2011, ApJ, 731, L25
  * Hartmann et al. (2016) Hartmann L., Herczeg G., Calvet N., 2016, ARA&A, 54, 135
  * Hatchell et al. (2007) Hatchell J., Fuller G. A., Richer J. S., 2007, A&A, 472, 187
  * Hennebelle & Falgarone (2012) Hennebelle P., Falgarone E., 2012, A&ARv, 20, 55
  * Heyer & Brunt (2004) Heyer M. H., Brunt C. M., 2004, ApJ, 615, L45
  * Hildebrand (1983) Hildebrand R. H., 1983, QJRAS, 24, 267
  * Keto (2003) Keto E., 2003, ApJ, 599, 1196
  * Kim & Kurtz (2006) Kim K.-T., Kurtz S. E., 2006, ApJ, 643, 978
  * Klessen & Hennebelle (2010) Klessen R. S., Hennebelle P., 2010, A&A, 520, A17
  * Kwan & Scoville (1976) Kwan J., Scoville N., 1976, ApJ, 210, L39
  * Lada (1985) Lada C. J., 1985, ARA&A, 23, 267
  * Larson (1981) Larson R. B., 1981, MNRAS, 194, 809
  * Lee (2020) Lee C.-F., 2020, A&ARv, 28, 1
  * Lee et al. (2000) Lee C.-F., Mundy L. G., Reipurth B., Ostriker E. C., Stone J. M., 2000, ApJ, 542, 925
  * Li et al. (2003) Li D., Goldsmith P. F., Menten K., 2003, ApJ, 587, 262
  * Li et al. (2015) Li H., et al., 2015, ApJS, 219, 20
  * Li et al. (2018) Li Q., et al., 2018, ApJ, 867, 167
  * Li et al. (2019) Li S., Zhang Q., Pillai T., Stephens I. W., Wang J., Li F., 2019, ApJ, 886, 130
  * Li et al. (2020) Li S., et al., 2020, arXiv e-prints, p. arXiv:2009.05506
  * Liu et al. (2016) Liu T., et al., 2016, ApJ, 824, 31
  * Liu et al. (2017) Liu T., et al., 2017, ApJ, 849, 25
  * Liu et al. (2020a) Liu T., et al., 2020a, MNRAS, 496, 2790
  * Liu et al. (2020b) Liu T., et al., 2020b, MNRAS, 496, 2821
  * López-Sepulcre et al. (2009) López-Sepulcre A., Codella C., Cesaroni R., Marcelino N., Walmsley C. M., 2009, A&A, 499, 811
  * López-Sepulcre et al. (2010) López-Sepulcre A., Cesaroni R., Walmsley C. M., 2010, A&A, 517, A66
  * López-Sepulcre et al. (2011) López-Sepulcre A., et al., 2011, A&A, 526, L2
  * Machida & Hosokawa (2013) Machida M. N., Hosokawa T., 2013, MNRAS, 431, 1719
  * Mangum & Shirley (2015) Mangum J. G., Shirley Y. L., 2015, PASP, 127, 266
  * Matzner & Jumper (2015) Matzner C. D., Jumper P. H., 2015, ApJ, 815, 68
  * Maud et al. (2015) Maud L. T., Moore T. J. T., Lumsden S. L., Mottram J. C., Urquhart J. S., Hoare M. G., 2015, MNRAS, 453, 645
  * McKee & Ostriker (2007) McKee C. F., Ostriker E. C., 2007, ARA&A, 45, 565
  * McKee & Tan (2003) McKee C. F., Tan J. C., 2003, ApJ, 585, 850
  * Molinari et al. (2002) Molinari S., Testi L., Rodríguez L. F., Zhang Q., 2002, ApJ, 570, 758
  * Mookerjea et al. (2007) Mookerjea B., Casper E., Mundy L. G., Looney L. W., 2007, ApJ, 659, 447
  * Motte et al. (2007) Motte F., Bontemps S., Schilke P., Schneider N., Menten K. M., Broguière D., 2007, A&A, 476, 1243
  * Nakamura & Li (2007) Nakamura F., Li Z.-Y., 2007, ApJ, 662, 395
  * Nakamura et al. (2011) Nakamura F., et al., 2011, ApJ, 726, 46
  * Narayanan et al. (2012) Narayanan G., Snell R., Bemis A., 2012, MNRAS, 425, 2641
  * Nony et al. (2020) Nony T., et al., 2020, A&A, 636, A38
  * Offner et al. (2011) Offner S. S. R., Lee E. J., Goodman A. A., Arce H., 2011, ApJ, 743, 91
  * Osorio et al. (1999) Osorio M., Lizano S., D’Alessio P., 1999, ApJ, 525, 808
  * Plunkett et al. (2013) Plunkett A. L., Arce H. G., Corder S. A., Mardones D., Sargent A. I., Schnee S. L., 2013, ApJ, 774, 22
  * Qiu et al. (2009) Qiu K., Zhang Q., Wu J., Chen H.-R., 2009, ApJ, 696, 66
  * Reiter et al. (2017) Reiter M., Kiminki M. M., Smith N., Bally J., 2017, MNRAS, 470, 4671
  * Riaz et al. (2017) Riaz B., Briceño C., Whelan E. T., Heathcote S., 2017, ApJ, 844, 47
  * Richer et al. (2000) Richer J. S., Shepherd D. S., Cabrit S., Bachiller R., Churchwell E., 2000, in Mannings V., Boss A. P., Russell S. S., eds, Protostars and Planets IV. p. 867 (arXiv:astro-ph/9904097)
  * Rosolowsky et al. (2008) Rosolowsky E. W., Pineda J. E., Kauffmann J., Goodman A. A., 2008, ApJ, 679, 1338
  * Shepherd & Churchwell (1996) Shepherd D. S., Churchwell E., 1996, ApJ, 457, 267
  * Shu et al. (1987) Shu F. H., Adams F. C., Lizano S., 1987, ARA&A, 25, 23
  * Shu et al. (2000) Shu F. H., Najita J. R., Shang H., Li Z. Y., 2000, in Mannings V., Boss A. P., Russell S. S., eds, Protostars and Planets IV. pp 789–814
  * Solomon et al. (1981) Solomon P. M., Huguenin G. R., Scoville N. Z., 1981, ApJ, 245, L19
  * Takahashi et al. (2019) Takahashi S., Machida M. N., Tomisaka K., Ho P. T. P., Fomalont E. B., Nakanishi K., Girart J. M., 2019, ApJ, 872, 70
  * Tan et al. (2014) Tan J. C., Beltrán M. T., Caselli P., Fontani F., Fuente A., Krumholz M. R., McKee C. F., Stolte A., 2014, in Beuther H., Klessen R. S., Dullemond C. P., Henning T., eds, Protostars and Planets VI. p. 149 (arXiv:1402.0919), doi:10.2458/azu_uapress_9780816531240-ch007
  * Urquhart et al. (2018) Urquhart J. S., et al., 2018, MNRAS, 473, 1059
  * Wang et al. (2010) Wang P., Li Z.-Y., Abel T., Nakamura F., 2010, ApJ, 709, 27
  * Wang et al. (2011) Wang K., Zhang Q., Wu Y., Zhang H., 2011, ApJ, 735, 64
  * Wang et al. (2014) Wang K., et al., 2014, MNRAS, 439, 3275
  * Watson et al. (2016) Watson D. M., et al., 2016, ApJ, 828, 52
  * Whelan et al. (2005) Whelan E. T., Ray T. P., Bacciotti F., Natta A., Testi L., Randich S., 2005, Nature, 435, 652
  * Williams et al. (2000) Williams J. P., Blitz L., McKee C. F., 2000, in Mannings V., Boss A. P., Russell S. S., eds, Protostars and Planets IV. p. 97 (arXiv:astro-ph/9902246)
  * Wu et al. (2004) Wu Y., Wei Y., Zhao M., Shi Y., Yu W., Qin S., Huang M., 2004, A&A, 426, 503
  * Yang et al. (2018) Yang A. Y., Thompson M. A., Urquhart J. S., Tian W. W., 2018, ApJS, 235, 3
  * Zapata et al. (2010) Zapata L. A., Schmid-Burgk J., Muders D., Schilke P., Menten K., Guesten R., 2010, A&A, 510, A2
  * Zhang et al. (2005) Zhang Q., Hunter T. R., Brand J., Sridharan T. K., Cesaroni R., Molinari S., Wang J., Kramer M., 2005, ApJ, 625, 864
  * Zhang et al. (2007) Zhang Q., Hunter T. R., Beuther H., Sridharan T. K., Liu S. Y., Su Y. N., Chen H. R., Chen Y., 2007, ApJ, 658, 1152
  * Zuckerman et al. (1976) Zuckerman B., Kuiper T. B. H., Rodriguez Kuiper E. N., 1976, ApJ, 209, L137
  * de Villiers et al. (2014) de Villiers H. M., et al., 2014, MNRAS, 444, 566

##  Appendix A A. Outflow lobes for all the regions

As presented in Section 3.1, we identified the outflow lobes using three
different tracers (i.e., CO, HCN, and HCO+). An example figure for CO and HCN
lobes is presented in Figure 1. Figures corresponding to the rest of the
regions with HCN lobes are presented in Figures 9 and 10. The figures with
HCO+ outflow lobes are also presented in Figures 11 and 12.

Figure 9: Images of HCN outflows in the six target fields. Background grey-
scale images are the ALMA 0.9 mm continuum maps. The red and blue contours
correspond to redshifted and blueshifted HCN gas integrated over carefully
selected velocity ranges to depict the outflow lobes. The blueshifted and
redshifted outflow lobes of HCN are marked by solid green and purple arrows,
respectively, while the outflow lobes identified using CO are also shown by
blue and red dashed arrows, respectively. The driving sources detected in the
continuum map are marked in yellow ellipses. Outflow lobes with yellow
diamonds are those for which no continuum sources are identified (see caption
of Figure 1).

Figure 10: Images of HCN outflows for the remaining four target fields (see
caption of Figure 9).

Figure 11: Images of HCO+ outflows in six target fields. The red and blue
contours correspond to redshifted and blueshifted HCO+ gas integrated over
carefully selected velocity ranges to depict the outflow lobes. The
blueshifted and redshifted outflow lobes of HCO+ are marked by solid cyan and
magenta arrows, respectively. The blueshifted and redshifted outflow lobes
identified using CO are shown by blue and red dashed arrows, while a few
additional blueshifted and redshifted lobes that are identified only in HCN
are marked by green and purple arrows, respectively. The remaining symbols are
the same as shown in Figure 1 and Figure 9.

Figure 12: Images of HCO+ outflows for the remaining five target fields. For
details of the markers and symbols see caption of Figure 11.