# Large-scale molecular gas distribution in the M17 cloud complex:
dense gas conditions of massive star formation?

Quang Nguyen-Luong  McMaster University, 1 James St N, Hamilton, ON, L8P 1A2,
Canada  Graduate School of Natural Sciences, Nagoya City University, Mizuho-
ku, Nagoya, Aichi 467-8601, Japan<EMAIL_ADDRESS>Fumitaka Nakamura
National Astronomical Observatory of Japan, 2-21-1 Osawa, Mitaka, Tokyo
181-8588, Japan  Department of Astronomy, The University of Tokyo, Hongo,
Tokyo 113-0033, Japan  The Graduate University for Advanced Studies
(SOKENDAI), 2-21-1 Osawa, Mitaka, Tokyo 181-0015, Japan  Koji Sugitani
Graduate School of Natural Sciences, Nagoya City University, Mizuho-ku,
Nagoya, Aichi 467-8601, Japan  Tomomi Shimoikura  Faculty of Social
Information Studies, Otsuma Women’s University, Chiyoda-ku,Tokyo, 102-8357,
Japan  Kazuhito Dobashi  Department of Astronomy and Earth Sciences, Tokyo
Gakugei University, 4-1-1 Nukuikitamachi, Koganei, Tokyo 184-8501, Japan
Shinichi W. Kinoshita  Department of Astronomy, The University of Tokyo,
Hongo, Tokyo 113-0033, Japan  National Astronomical Observatory of Japan,
2-21-1 Osawa, Mitaka, Tokyo 181-8588, Japan  Kee-Tae Kim  Korea Astronomy &
Space Science Institute, 776 Daedeokdae-ro, Yuseong-gu, Daejeon 34055,
Republic of Korea  University of Science and Technology, Korea (UST), 217
Gajeong-ro, Yuseong-gu, Daejeon 34113, Republic of Korea  Hynwoo Kang  Korea
Astronomy & Space Science Institute, 776 Daedeokdae-ro, Yuseong-gu, Daejeon
34055, Republic of Korea  Patricio Sanhueza  National Astronomical Observatory
of Japan, 2-21-1 Osawa, Mitaka, Tokyo 181-8588, Japan  Neal J. Evans II  Korea
Astronomy & Space Science Institute, 776 Daedeokdae-ro, Yuseong-gu, Daejeon
34055, Republic of Korea  Department of Astronomy, The University of Texas at
Austin, 2515 Speedway, Stop C1400, Austin, TX 78712-1205, USA  Glenn J. White
Department of Physics and Astronomy, The Open University, Walton Hall, Milton
Keynes, MK7 6AA, UK  RAL Space, STFC Rutherford Appleton Laboratory, Chilton,
Didcot, Oxfordshire, OX11 0QX, UK

(Received October 23,2019; Accepted January 15, 2020)

###### Abstract

The non-uniform distribution of gas and protostars in molecular clouds is
caused by combinations of various physical processes that are difficult to
separate. We explore this non-uniform distribution in the M17 molecular cloud
complex that hosts massive star formation activity using the 12CO ($J=1-0$)
and 13CO ($J=1-0$) emission lines obtained with the Nobeyama 45m telescope.
Differences in clump properties such as mass, size, and gravitational
boundedness reflect the different evolutionary stages of the M17-HII and
M17-IRDC clouds. Clumps in the M17-HII cloud are denser, more compact, and
more gravitationally bound than those in M17-IRDC. While M17-HII hosts a large
fraction of very dense gas (27%) that has column density larger than the
threshold of $\sim$ 1 g cm-2 theoretically predicted for massive star
formation, this very dense gas is deficient in M17-IRDC (0.46%). Our HCO+
($J=1-0$) and HCN ($J=1-0$) observations with the TRAO 14m telescope, trace
all gas with column density higher than $3\times 10^{22}$ cm-2, confirm the
deficiency of high density ($\gtrsim 10^{5}$ cm-3) gas in M17-IRDC. Although
M17-IRDC is massive enough to potentially form massive stars, its deficiency
of very dense gas and gravitationally bound clumps can explain the current
lack of massive star formation.

###### Subject headings:

stars: formation, ISM: clouds, ISM: structure, (ISM:) evolution, methods:
observational

##  1\. Introduction

Figure 1.— Three-color image of the M17 region as seen at 24 µm in MJy/sr
(red, Spitzer), 250 µm in Jy/beam (green, Herschel), and 870 µm in Jy/beam
(blue, APEX).

The discovery of Carbon monoxide (CO) in the interstellar medium opened a new
window into the molecular gas universe (Wilson et al., 1970). Understanding
how molecular gas is organized into structures is important since it has an
essential role in nurturing the star and planet formation process. Besides a
degree-resolution all-sky area in CO ($J=1-0$) (Dame et al., 2001), numerous
higher resolution wide-field CO surveys have presented three-dimensional (3D)
space-space-velocity structures of the molecular gas environments in more
detail (e.g. Schneider et al. 2010; Carlhoff et al. 2013; Dempsey et al. 2013;
Barnes et al. 2015). Wide-field mapping of denser gas tracers, i.e., HCO+,
HCN, CS also probe into the inner dense region of the molecular clouds (Wu et
al., 2010; Kauffmann et al., 2017; Pety et al., 2017).

Using one of the world’s largest mm-waveband telescopes, the 45m telescope of
the Nobeyama Radio Observatory (NRO 45m), we have conducted a high spatial
resolution, highly sensitive, large dynamical range wide-field survey of CO
($J=1-0$) and other gases to explore the molecular cloud structure in both
low-mass and high-mass star-forming regions. The project is named “Star
Formation Legacy project” and presented in Nakamura et al. (2019b). The
detailed observational results for the individual regions are given in other
articles (Orion A: Ishii et al. 2019, Tanabe et al. 2019; Nakamura et al.
2019a; M17: Shimoikura et al. 2019, Sugitani et al. 2019; Aquila Rift:
Shimoikura et al. 2018, Kusune et al. 2019; other regions: Dobashi et al.
2019a, Dobashi et al. 2019b).

As a part of the project, we investigate the global molecular gas distribution
of the M17 region to understand the role of dense gas in star formation in M17
(see Figure 1 for the wide-field infrared and submillimeter image of the
mapped region). The 12CO ($J=1-0$) and 13CO ($J=1-0$) data from the NRO 45m
telescope is complemented by HCN ($J=1-0$) and HCO+ ($J=1-0$) observed by the
Taeduk Radio Astronomy Observatory (TRAO) 14m telescope. We summarize an
overview of the M17 complex in Section 2. Section 3 describes the detailed
observations and data used in this paper. The global structure of molecular
gas and dense gas is examined in Section 4 and the role of dense gas in star
formation in M17 is discussed in Section 5. Finally, we summarize the results
in Section 6.

##  2\. Overview of the M17 region

The M17 complex is a $\sim 2\arcdeg\times 2\arcdeg$ molecular cloud complex
surrounding M17 nebula (also called The Omega, or The Horseshoe, or The Swan
nebula) and is located in the Sagittarius spiral arm (Elmegreen et al., 1979;
Reid et al., 2019). The M17 nebula is excited by 1 Myr-old the NGC 6618 loose
(radius $>$ 1 pc) open cluster which contains hundreds of stars with spectral
types earlier than B9 (Lada et al., 1991). The MYStIX (Massive Young Star-
Forming Complex Study in IR and X-ray) survey with the Chandra X-Ray
Observatory counted a total of 16000 stars in NGC 6618 (M17) cluster (Kuhn et
al., 2015). It is the second most populated cluster after the Carina cluster
in the MYStIX survey (Kuhn et al., 2015). For comparison, while NGC 6611 and
Orion Nebula Cluster have peak stellar surface densities of around 10–100
stars pc-2, that of NGC 6618 is remarkably much higher, $>1000$ stars pc-2.
The HII region (M17-HII region) surrounding the cluster has opened a large gap
at its edge which lets stellar radiation and winds escape exciting a diffuse
X-Ray emitting region observed with the Chandra Observatory (Townsley et al.,
2003). In addition to the relatively mature HII region around the cluster,
other notable star-forming regions have been discovered in the vicinity of M17
such as the immediate environment of M17 (Ando et al., 2002), or the M17
Infrared Dark Cloud (M17-IRDC, also known as M7-SWex) that contains the IRDC
G14.225-0.506 (Povich & Whitney, 2010; Povich et al., 2016; Ohashi et al.,
2016). M17 forms a larger molecular cloud complex together with M16 cloud as
suggested by Nguyen-Luong et al. (2016).

Parallax distances of $1.83^{+0.08}_{-0.07}$ kpc and $1.98^{+0.14}_{-0.12}$
kpc by maser monitoring have been determined towards two dust clumps in M17,
G014.63-00.57 and G015.03-00.57, respectively (Honma et al., 2012; Wu et al.,
2014). These parallax distances are larger than photometric distances of
$1.3\pm 0.4$ kpc (Hanson et al., 1997) and $1.6\pm 0.3$ kpc (Nielbock et al.,
2001), obtained by the analysis of the main-sequence OB stars. Note that Chini
et al. (1980) derived a distance of 2.2 kpc to M17 based on the multi-colour
photometry. Other parallax measurements to M17-HII region have suggested a
distance of 2.0 kpc (Xu et al., 2011), $1.9\pm 0.1$ kpc (Wu et al., 2019), or
$2.04^{+0.16}_{-0.17}$ kpc(Chibueze et al., 2016). To be consistent with other
papers in our project (Sugitani et al., 2019; Shimoikura et al., 2019), we
adopt 2 kpc to be the distance to the entire M17 complex.

Figure 2.— The M17 complex in (upper) 12CO ($J=1-0$) and (lower) 13CO
($J=1-0$) from the NRO 45m star formation survey, integrated over the entire
velocity range (from -20 to 60  km s-1). The red rectangles outline the two
prominent star-forming regions: M17-HII (left) and M17-IRDC (right). The star
symbols pinpoint the OB star clusters responsible for the giant HII region.
The cross symbols pinpoint the locations of the massive cores (M $>$ 500 $\rm
M_{\sun}$) reported by Shimoikura et al. (2019).

Elmegreen et al. (1979) found a velocity gradient in the M17 molecular cloud
complex from north-east to south-west based on the low-resolution 12CO
($J=1-0$) and 13CO ($J=1-0$) observations. They suggested that the gradient
may be an outcome of the recent passage of a Galactic spiral density wave,
which has triggered star formation in M17. The spiral density waves compress
the interstellar gas, promoting the formation of giant molecular clouds. The
spiral density waves also enhance the collision rates of molecular clouds,
which can trigger massive star formation and star cluster formation
efficiently (Scoville et al., 1986; Tan, 2000; Nakamura et al., 2012; Wu et
al., 2017; Fukui et al., 2014; Dobashi et al., 2019b). In fact, Nishimura et
al. (2018) recently found evidence for a cloud-cloud collision near the
M17-HII region. However, streaming motions from the spiral waves can also
inhibit massive star formation, a fact that has been seen in other galaxies
such as M51 (Meidt et al., 2013). In either case, the M17 complex, as a whole,
is well-suited to study the effect of dynamical compression of interstellar
gas by the Galactic spiral density wave. Interestingly, a second compression
at the interface of the M17-HII region when the OB star clusters compress the
edge of the cloud also occured which can be seen in CO ($J=3-2$) (Rainey et
al., 1987) and also in high density tracer such as HCN ($J=4-3$) (White et
al., 1982).

From the Spitzer observations, Povich & Whitney (2010) and Povich et al.
(2016) discovered that the mass function of young stellar objects (YSOs)
around the M17-HII region seems consistent with the Salpeter IMF, whereas that
in the M17-IRDC is significantly steeper than the Salpeter IMF. In other
words, the high-mass stellar population in M17-IRDC is deficient. This fact
makes the M17 region an attractive test case for models of high-mass star
formation.

##  3\. Observations

###  3.1. CO observations from the NRO 45m star formation project

The data come from the NRO 45m star formation project (PI: Fumitaka Nakamura)
which observed 12CO ($J=1-0$), 13CO ($J=1-0$), C18O ($J=1-0$), N2H+ ($J=1-0$)
and CCS ($J_{N}=8_{7}-7_{6}$) lines toward a sample of star-forming regions:
M17, Orion, and Aquila Rift. M17 is the most distant star-forming region in
the survey. We carried out the mapping observations toward M17 between
December 2014 and March 2017. The three CO isotopologue lines were observed
using the four-beam dual polarization, sideband-separating SIS FOREST receiver
(Minamidani et al., 2016). However, the C18O coverage is smaller than those of
12CO and 13CO, due to malfunction of a sub-reflector system in one period of
observations. See Nakamura et al. (2019b) for more details of the
observations.

The telescope HPBW beam size is $\sim 15\arcsec$ and the main-beam efficiency
$\eta_{\rm MB}$ $\sim 40\%$ at 115 GHz. We used the SAM45 spectrometer as the
backend which provided a bandwidth of 63 MHz and a frequency resolution of
15.26 kHz, corresponding to a velocity resolution of $\sim
0.04\,\mbox{\,km$\,$s${}^{-1}$}$. Standard on-the-fly (OTF) mapping techniques
were used to carry out the mapping observations. Details on the observation
procedure such as OFF-positions, submapping integration time, efficiency can
be found in Nakamura et al. (2019b).

The raw data were reduced by the NRO data reduction tool, NOSTAR. All three
12CO, 13CO, and C18O data were convolved to $22\arcsec$ beam size and
reprojected to a common $7.5\arcsec\times 7.5\arcsec$ grid to facilitate our
analysis. Figure 2 shows the 12CO and 13CO intensity maps integrated from –20
km s-1 to 60  km s-1. The rms noise levels were calculated as the average of
the emission-free channels from –18 to –11  km s-1. The average rms of 12CO,
13CO, and C18O are $\sim$ 1.0, 0.4, and 0.3 K per 7.5″-pixel and per 0.1  km
s-1channel, respectively (Figure 3). The data are available
online111http://jvo.nao.ac.jp/portal/v2/.

Figure 3.— The histograms of the rms maps of 12CO ($J=1-0$) (red), 13CO
($J=1-0$) (green), and C18O ($J=1-0$) (blue) of the M17 data.

$\begin{array}[]{ccc}\includegraphics[width=170.71652pt]{M17HII_spec.eps}&\includegraphics[width=170.71652pt]{M17SWex_spec.eps}&\includegraphics[width=170.71652pt]{M17all_spec.eps}\par\end{array}$

Figure 4.— The 12CO ($J=1-0$) (blue) and 13CO ($J=1-0$) (magenta) spectra
averaged over the (a) M17-HII, (b) M17-IRDC, and (c) entire M17 regions. Red
curves are three-Gaussian component models best fitting the data, and the
fitted parameters are listed in Table 1.

###  3.2. HCO+ and HCN observations with TRAO 14m telescope

HCO+ ($J=1-0,89188.526~{}{\rm MHz}$) and HCN ($J=1-0,88631.6023$ MHz) lines
were observed simultaneously with the TRAO 14m telescope. The telescope was
equipped with the SEQUOIA receiver with 16 pixels in $4\times 4$ array. The
2nd IF modules with the narrowband and the eight channels with 4 FFT
spectrometers allow observing 2 frequencies simultaneously within the 85–100
or 100–115 GHz frequency ranges for all 16 pixels. We carried out the M17
observations between December 2016 and December 2017. Observations were done
in the (On-The-Fly) OTF mode, and the native velocity resolution is about 0.05
km s-1 (15 kHz) per channel, and their full spectral bandwidth is 62.5 MHz
with 4096 channels. The telescope HPBW beam size is $\sim 50\arcsec$ at 100
GHz and the main-beam efficiency $\eta_{\rm MB}$ is $\sim 46\%$ at 89 GHz. The
system temperature was in the range 150 – 300 K. The cube was regridded to a
$7.5\arcsec\times 7.5\arcsec$ grid.

##  4\. Results

###  4.1. Multiple cloud ensemble along the line of sight

The 12CO ($J=1-0$) and 13CO ($J=1-0$) intensity maps integrated over the
entire velocity range from -20 to 60  km s-1 in Figure 2 show the global
molecular gas distribution of the M17 complex. The maps cover an area of
$1.72\arcdeg\times 0.53\arcdeg$ from $13.67\arcdeg$ to $15.39\arcdeg$ in
Galactic longitude and from $-0.80\arcdeg$ to $-0.27\arcdeg$ in Galactic
latitude. The CO emission around the HII region encompassing the NGC 6618
cluster stands out as the brightest subregion in the map. Also, the emission
from the M17-IRDC region is notable in both 12CO and 13CO.

The integrated spectra of 12CO and 13CO towards the M17 complex show three
major peaks over the complete velocity ranging from -20 to 60 km s-1 (see
Figure 4c). The averaged spectra are fitted with three velocity components
which show three main components peaking at $\sim 20$ km s-1, $\sim 38$ km
s-1, and $\sim$57  km s-1; velocity dispersion
$\sigma=\frac{FWHM}{2\sqrt{2ln2}}$ of $\sim$4.0  km s-1, 6.9  km s-1, and 3.7
km s-1, respectively. The fitted parameters are summarized in Table 1. It
seems that the main component centers around $\sim$20  km s-1 and the other
two components center around $\sim$ 38  km s-1 and $\sim$ 57  km s-1. This
becomes more obvious when comparing the 12CO and 13CO spectra averaged over
the entire M17 complex with those of the individual regions M17-HII and
M17-IRDC (Figure 4a-b). Each spectrum in these three panels show distinct
peaks at different velocities, but the dominant peak is around $V_{\rm
LSR}\sim 20\,\mbox{\,km$\,$s${}^{-1}$}$, which is the main velocity peak of
both M17-HII and M17-IRDC regions (Elmegreen et al., 1979). In the average
spectrum, we can find at least four groups of molecular clouds in the LSR
velocity ranges $<10$ km s-1, $10-30$ km s-1, $30-50$ km s-1, and $>50$ km
s-1.

We further examine the distances of dense clumps in M17 detected with ATLASGAL
survey (Schuller et al., 2010; Csengeri et al., 2017). The distances were
measured by Wienen et al. (2015) and Urquhart et al. (2018) using the
kinematic method and cross-correlating with maser parallax measurements if
available to resolve the distance ambiguity issues. Approximately 90% of
sources have distances of 1.8 or 1.9 kpc (Figure 5), which implies that most
clumps in M17 region are located around $1.9\pm 0.1$ kpc and the region migh
have a depth of $\sim 0.1-0.3$ kpc. The distances of dense clumps are close to
our assumption of 2 kpc. The 12CO and 13CO position-velocity diagrams show
that the emission lines smoothly change from M17-HII and M17-IRDC in terms of
the radial velocity, line width, and intensity, indicating that the two
subregions are physically connected (Shimoikura et al., 2019). Thus, the
assumption that the two subregions are located at the same distance $\sim$2
kpc is justifiable.

Figure 5.— Histogram of distances of ATLASGAL sources extracted from Urquhart et al. (2018). Table 1Gaussian parameters best fitting the averaged spectra in Figure 4. Line | Component | Parameters | M17 Entire | M17-HII | M17-IRDC
---|---|---|---|---|---
12CO ($J=1-0$) | Gaussian 1 | T [K] | 10.9 | 9.1 | 10.5
$V_{\rm LSR}$ [km s-1] | 20.3 | 20.0 | 20.1
$\sigma_{v}$ [km s-1] | 4.1 | 2.3 | 3.7
Gaussian 2 | T [K] | 4.9 | 4.1 | 5.9
$V_{\rm LSR}$ [km s-1] | 38.0 | 31.3 | 36.6
$\sigma_{v}$ [km s-1] | 6.9 | 11.7 | 7.4
Gaussian 3 | T [K] | 1.9 | 1.8 | 2.1
$V_{\rm LSR}$ [km s-1] | 57.0 | 57.0 | 59.3
$\sigma_{v}$ [km s-1] | 3.7 | 2.4 | 4.7
13CO ($J=1-0$) | Gaussian 1 | T [K] | 2.8 | 2.1 | 3.5
$V_{\rm LSR}$ [km s-1] | 20.6 | 19.8 | 20.2
$\sigma_{v}$ [km s-1] | 3.0 | 2.0 | 2.4
Gaussian 2 | T [K] | 0.8 | 0.8 | 0.9
$V_{\rm LSR}$ [km s-1] | 36.7 | 30.5 | 34.1
$\sigma_{v}$ [km s-1] | 6.3 | 8.3 | 6.9
Gaussian 3 | T [K] | 0.2 | 1.6 | 0.2
$V_{\rm LSR}$ [km s-1] | 57.4 | 68.1 | 57.6
$\sigma_{v}$ [km s-1] | 2.6 | 1.8 | 2.8

In Appendix A (Figures 18 and 19), we show the 12CO and 13CO ($J=1-0$)
intensity maps, respectively, integrated over the velocity ranges from $-20$
to 10 km s-1, 10 to 30 km s-1, 30 to 50 km s-1, and 50 to 60 km s-1. The bulk
of the M17 emission is seen in the velocity range 10–30  km s-1 in both 12CO
and 13CO ($J=1-0$) lines. M17-HII region is especially bright in the 12CO
($J=1-0$) maps and the M17-IRDC region is more prominent in 13CO ($J=1-0$).
The emission in the range 30–50  km s-1 is stronger toward the Galactic
equator and distributed over a larger region on the plane of the sky. BeSSeL
parallax-Based Distance Calculator confirmed that the main velocity component
10–30  km s-1 is more likely to be in the Sagittaurius arm whereas the 30–50
km s-1 is more likely to be in between the Scutum arm and Norma arms (Reid et
al., 2016, 2019). Therefore, while the main component is likely at a distance
of $\sim 2$ kpc, the 30–50  km s-1 is in between 3 and 4 kpc. The emission in
the range $>50$ km s-1 is more scattered and does not appear to correlate with
the main bulk emission of M17. The 13CO integrated intensity map in the
velocity range 10–30  km s-1 also coincides with the sub-mm emission observed
with ATLASGAL (see the three-color image of Figure 1). Subsequently, we
consider only the emission around 10–30  km s-1, as a part of M17, and used it
to derive physical quantities.

###  4.2. Temperature and column density distribution

Here we derive the excitation temperature, column density, and optical depth
of the main cloud component of M17 (10–30  km s-1) using our CO data (see also
Mangum & Shirley 2015 for the derivation of these physical quantities). Based
on the differences of 12CO and 13CO in the global integrated maps, we divide
these cubes using different masked regions. Mask 1 is set to the region where
both of the 12CO and 13CO integrated intensities are more than three times as
high as the noise levels of 0.22 K  km s-1 in the 12CO integrated map and
0.089 K  km s-1 in the 13CO integrated maps. Mask 2 is set to the region where
only the 12CO emission is detected above 3$\sigma$ = 0.27 K  km s-1. Mask 3 is
set to the region where neither 12CO nor 13CO is detected above 3$\sigma$
levels, which can be regarded as an emission-free region.

We assume that 12CO and 13CO having the same excitation temperature $T_{\rm
ex}$, and that it can be calculated from the maximum main-beam brightness
temperature of 12CO, $T_{\rm max}({\rm{}^{12}CO})$, in the masked region 1 as

$T_{\rm ex}=\frac{5.5\,{\mbox{\,K}}}{{\rm ln}\left(1+5.5\,{K}/\left(T_{\rm
max}({\rm{}^{12}CO})+0.82\mbox{\,K}\right)\right)}\,.$ (1)

The optical depth $\tau({\rm{}^{13}CO})$ as a function of velocity in the
masked region 1 can be derived from the main-beam brightness temperature
$T_{\rm MB}({\rm{}^{13}CO})$ as

$\tau({\rm{}^{13}CO})(v)=-\ln\left(1-\frac{T_{\rm
MB}({\rm{}^{13}CO})}{f\left(J(T_{\rm ex})-J(2.7\,K\right))}\right)\,.$ (2)

where the filling factor $f=1$ is assumed as the extended nature of the
emission and $J(T_{\rm ex})=\frac{h\nu}{k\left({\rm exp}\left(h\nu/kT_{\rm
ex}\right)-1\right)}=\frac{5.3}{\left({\rm exp}\left(5.3/T{\rm
ex}\right)-1\right)}$ for the 13CO ($J=1-0$) emission. $h,k,\nu$ are Planck
constant, Boltzmann constant, and transitional frequency. Subsequently, the
13CO column density can be derived as

$\displaystyle N({\rm{}^{13}CO})$ $\displaystyle=$
$\displaystyle\frac{3h}{8\pi^{3}S\mu^{2}}\frac{Q_{\rm rot}}{g_{\rm
u}}\frac{{\rm exp}{(5.3/T_{\rm ex})}}{{\rm exp}{(5.3/T_{\rm
ex}-1)}}\int\tau(v)dv\,,$ (3)

where $\mu=0.112$ D, $S=\frac{J_{\rm u}}{2J_{\rm u}+1}$ with $J_{\rm u}=1$,
and $g_{\rm u}=2J_{\rm u}+1=3$. The rotational partition function is
approximated as $Q_{\rm rot}=\frac{kT_{\rm ex}}{hB}+\frac{1}{3}$ with
$B=55101.011$ MHz with the assumption that all the levels have the same
$T_{\rm ex}$ and that $T_{\rm ex}$ is much higher than 5.3 K. This assumption
might be wrong but is conventional, which is commonly called the LTE
approximation. We convert the 13CO column density to H2 column density
assuming a 13CO fractional abundance of $2\times 10^{-6}$ (Dickman, 1978). We
use the updated conversion ratio $N_{\rm H_{2}}/N_{\rm CO}=6000$ from Lacy et
al. (2017), which yielded a 13CO fractional abundance of $2.5\times 10^{-6}$
using the ratio 12C/ 13C = 60 specifically calculated for M17 (Henkel et al.,
1982) and agreed with the average Galactic value (Langer & Penzias, 1990).

For mask 2 region, we use the 12CO ($J=1-0$) emission as a proxy to estimate
the H2 column density assuming that the emission is optically thin as $N_{\rm
H_{2}}=XW({\rm CO})$ where $X$ is the conversion factor and $W$(CO) is the
12CO ($J=1-0$) integrated intensity. The $X$ factor $X=2\times 10^{20}~{}{\rm
cm}^{-2}$ ${\rm K}^{-1}\,(\rm\mbox{\,km$\,$s${}^{-1}$})^{-1}$ is used as
recommended by Bolatto et al. (2013), which was established after an
exhaustive investigation of all possible measurements. Note that we do not
estimate $T_{\rm ex}$ for this masked region.

Excluding pixels where excitation temperature is less than 2.7 K (14% number
of pixels) caused by low rms in the integrated maps and using only non-zero
data within the percentile range 0.5%–99.99%, we obtain a temperature range of
8 – 81 K, and an $N_{\rm H_{2}}$ range of $3\times 10^{20}$– $4.6\times
10^{23}$ cm-2 for the entire M17 complex (Figures 6 and 7). The median CO
excitation temperature is $\sim 20$ K, and median column density is $1.3\times
10^{22}$ cm-2 for the entire M17 (Table 2).

$\begin{array}[]{ccc}\hskip-8.5359pt\includegraphics[width=173.56198pt]{Texhist.eps}&\hskip-8.5359pt\includegraphics[width=173.56198pt]{NH2hist.eps}&\hskip-8.5359pt\includegraphics[width=173.56198pt]{tau13COmaxhist.eps}\\\
\\\ \end{array}$

Figure 6.— The histograms of the (a) CO excitation temperature, (b) the H2
column density, and (c) the peak optical depth of the M17 region. The blue,
orange, and green histograms are toward the entire M17, M17-HII, and M17-IRDC,
respectively.

Figure 7.— The maps of the (a) CO excitation temperature, (b) H2 column density, and (c) peak optical depth of the M17 complex. We use only data within the main velocity range (10–30  km s-1) of M17. The red rectangles outline the two prominent star-forming regions: M17-HII (left) and M17-IRDC (right). The star symbol pinpoints the OB star cluster responsible for the giant HII region. The cross symbols pinpoint the locations of the massive cores (M $>$ 500 $\rm M_{\sun}$) reported by Shimoikura et al. (2019). Table 2Properties of the sub-region M17-HII, M17-IRDC and the M17 complex. $T_{\rm ex}$, $N_{\rm H_{2}}$, and $\Sigma_{\rm gas}$ are measured per pixel. Region | ($T_{\rm ex}^{\rm min},T_{\rm ex}^{\rm median},T_{\rm ex}^{\rm max}$) | ($N_{\rm H_{2}}^{\rm min},N_{\rm H_{2}}^{\rm median},N_{\rm H_{2}}^{\rm max}$) | ($\Sigma^{\rm min}_{\rm gas},\Sigma^{\rm median}_{\rm gas},\Sigma^{\rm max}_{\rm gas}$) | $A_{\rm tot}$ | $M_{\rm tot}$ | $n^{\rm mean}_{\rm H_{2}}$
---|---|---|---|---|---|---
| (K) |  $(10^{21}$ cm${}^{-2})$ | ($\rm M_{\sun}$pc${}^{-2})$ | (pc2) | ($\rm M_{\sun}$) | (cm-3)
M17-HII | (8, 15, 81) | (0.3, 6.3, 458) | (6, 134, 9831) | 364 | $1.43\times 10^{5}$ | 166
M17-IRDC | (11, 21, 41) | (1.9, 17, 93) | (40, 355, 1995) | 593 | $3.06\times 10^{5}$ | 285
M17 | (8, 20, 81) | (0.3, 13, 458) | (6,281,9831) | 957 | $4.49\times 10^{5}$ | 201

There are strong difference between M17-HII and M17-IRDC. First, the peak
optical depth $\tau_{\rm{}^{13}CO}^{\rm max}$ is remarkably different in two
regions as seen in Figure 6 and 7. $\tau_{\rm{}^{13}CO}^{\rm max}$ in M17-HII
has a mean value of 0.31 and a standard deviation of 0.15 while those of
M17-IRDC are 0.5 and 0.24, respectively. Some regions in M17-IRDC have
$\tau_{\rm{}^{13}CO}^{\rm max}$ values that are even higher than 1. Second,
M17-HII has much higher temperature than M17-IRDC. The maximum temperature in
M17-HII region reaches 81 K, especially around the NGC 6618 cluster, whereas
the maximum temperature in M17-IRDC is only 41 K (Figure 7a). Third, beside
being colder, M17-IRDC also has lower peak column density compared to M17-HII
(Figure 7b). We caution that the column density is probably over-estimated
because the partition function is overestimated due to the fact that $T_{ex}$
is not much greater than 5.5 K.

###  4.3. Dense gas mass function and mass properties

Figure 7b clearly shows that M17-HII has higher column density materials than
M17-IRDC. The dense gas mass function (DGMF) is useful to see how the dense
gas is concentrated in specific density ranges. Dividing the column density
into 120 column density bins ranging from $10^{21}$ cm-2 to $10^{24}$ cm-2 we
create the DGFMs as the normalized Cumulative Mass Distribution (CMD) as:

${\rm DGMF(>N_{\rm H_{2}})}=\frac{\int_{N_{\rm H_{2}}}^{N_{\rm H_{2}}^{\rm
max}}\mu m_{\rm H}A({N_{\rm H_{2}}})dN_{\rm H_{2}}}{M_{\rm tot}}\ ,$ (4)

where $N_{\rm H_{2}}$ is the column density from which the mass is
accumulated, $\mu=2.8$ is the mean molecular weight, $m_{\rm H}$ is the
hydrogen atomic mass, $A$ is the integrated area, and $N_{\rm H_{2}}^{\rm
max}$ is the maximum column density observed in the region. When $N_{\rm
H_{2}}$ reaches the noise level $N_{\rm H_{2}}^{\rm min}$, the obtained mass
is the total mass of the cloud $M_{\rm tot}$ in both masks 1 and 2. $N_{\rm
H_{2}}^{\rm min}$ is the column density corresponding to the 3$\sigma$ (Table
2). DGMFs for M17, M17-HII, and M17-IRDC are plotted in a log-log scale in
Figures 8. They have flat profiles below the column density $\sim 7\times
10^{21}$ cm-2 and then a quick drop to a power-law tail at higher column
density. The column density $\sim 7\times 10^{21}$ cm-2 is argued to be the
threshold for star formation (André et al., 2010; Arzoumanian et al., 2011;
Lada et al., 2010; Heiderman et al., 2010; Evans et al., 2014). The slope of
the power-law tail is shallower in M17-HII region than in M17-IRDC region.
DGMFs converge to unity toward lower column density but diverge at higher
column density. These profiles are similar to CMDs and DGMFs of other regions
(Lada et al. 2010, Kainulainen et al. 2013, and Rivera-Ingraham et al. 2015).

We calculate the very dense gas mass fraction, the ratio of mass that has
column density higher than $1\times 10^{23}\,\mbox{cm${}^{-2}$}$ or 1g/cm2, a
column density level suggested as massive star formation threshold (Krumholz &
McKee, 2008) as:

$f_{\rm verydense}=\frac{M_{\rm N_{H2}>1\times
10^{23}\,\mbox{cm${}^{-2}$}}}{M_{\rm tot}}$ (5)

In total, $f_{\rm verydense}$ is about 8.86% in the entire M17. Individually,
$f_{\rm verydense}$ is 27% in M17-HII and only 0.46% in M17-IRDC region.

On the other hand, the DGMFs for mass with column density above the presumed
threshold for star formation $7\times 10^{21}~{}\,\mbox{cm${}^{-2}$}$ are 96%,
91%, and 98.5% for M17, M17-HII, and M17-IRDC, respectively. These two column
density levels are marked as vertical lines in Figures 8.

The flat plateau of the DGMF at the low column density part gives us the total
masses of M17, M17-HII and M17-IRDC (Figure 8) which are captured in Table 2.
The total mass of M17 is $4.49\times 10^{5}$ $\rm M_{\sun}$, that of M17-HII
is $1.43\times 10^{5}$ $\rm M_{\sun}$, and that of M17-IRDC is $3.06\times
10^{5}$ $\rm M_{\sun}$. The median pixel-by-pixel mass surface density of
M17-IRDC (614 $\rm M_{\sun}$ pc-2) is higher than that of the M17-HII region
(280 $\rm M_{\sun}$ pc-2). However, the peak mass surface density is much
higher in M17-HII (9831 $\rm M_{\sun}$pc-2) than in M17-IRDC (1995 $\rm
M_{\sun}$pc-2) (see Table 2). The mean column density $N^{\rm mean}_{\rm
H_{2}}$ is calculated as

$N^{\rm mean}_{\rm H_{2}}=\frac{M_{\rm tot}}{A_{\rm tot}}$ (6)

where $A_{\rm tot}$ is the total areas of the clouds calculated as the sum of
areas of all interior pixels of the cloud projection on the plane of the sky.
We also calculate the volume density as
$n_{H_{2}}=\frac{M}{V}=\frac{M}{\frac{4}{3}\pi r^{3}}$ where
$r=\sqrt{\frac{A_{\rm tot}}{\pi}}$ and $A_{\rm tot}$ is tabulated in Table 2.
The mean volume density is higher in M17-IRDC (285 cm-3) than in M17-HII (166
cm-3). For the two column density levels discussed above, the corresponding
average volume densities of M17-HII, M17-IRDC, and entire M17, respectively,
are $1.0\times 10^{5}$, $2.1\times 10^{5}$, and $9.5\times 10^{4}$ cm-3 for
column density above $1\times 10^{23}\,\mbox{cm${}^{-2}$}$, and $3.5\times
10^{3}$, $1.7\times 10^{3}$, $1.5\times 10^{3}$ cm-3 for column density above
$7\times 10^{21}\,\mbox{cm${}^{-2}$}$.

In summary, the peak column density is higher in M17-HII while the average
column density and total mass are higher in M17-IRDC. If very dense gas is
considered alone, its average volume density is approximately two times more
in M17-HII than in M17-IRDC.

$\begin{array}[]{c}\includegraphics[width=455.24408pt]{DGMFnormalized.eps}\\\
\end{array}$

Figure 8.— The normalized cumulative column density distributions of the entire M17, M17-HII, and M17-IRDC regions. The vertical lines mark the locations of the column densities of $7\times 10^{21}$cm-2 and $1\times 10^{23}$cm-2.  Table 3Statistics of individual clumps extracted with the Dendrogram technique in the M17-HII, M17-IRDC, and the entire M17 clouds Region | $n_{\rm sources}$ | $n_{\rm sources}(\alpha_{\rm vir}>1)$ | ($R^{min},R^{median},R^{max}$) | ($M^{min},M^{median},M^{max}$) | ($\alpha_{\rm vir}^{min},\alpha_{\rm vir}^{median},\alpha_{\rm vir}^{max}$)
---|---|---|---|---|---
|  |  | pc | $M_{\odot}$ |
M17-HII | 26 | 11 | (0.12, 0.2, 0.41) | (3, 60, 1719) | (0.12, 0.86, 8.23)
M17-IRDC | 164 | 105 | (0.11, 0.2, 0.54) | (1, 14, 1001) | (0.14, 1.36, 14.23)
M17-Entire | 190 | 116 | (0.11, 0.2, 0.54) | (1, 17, 1719) | (0.12, 1.33, 14.23)

###  4.4. Individual clump structure extracted with Dendrogram

To obtain an automatic extraction of the individual clumps in M17, we use the
Dendrogram (Rosolowsky et al.,
2008)222https://Dendrograms.readthedocs.io/en/stable/. It is a hierarchical
clustering method that builds up clusters in a tree-like structure where each
node represents a leaf (structure that has no sub-structure) or branch
(structure that has successor structure). Each node in the cluster tree
contains a group of similar data. Clusters at one level join with clusters in
the next level up using a degree of similarity. The total number of clusters
is not predetermined.

In our extraction, we use Dendrogram to detect and extract the morphologies of
individual clumps using the 12CO (J = 1-0) data cube in the velocity range of
10.0 to 30.0 km s-1. We extract only sources in the regions that have column
density above $3\times 10^{22}\,\rm cm^{-2}$ as in the masked map (Figure 17).
This threshold corresponds to 3 times the median column density of the entire
map (Table 2). Dendrogram requires three input parameters: min_value,
min_delta, and min_npix. The first parameter min_value specifies the minimum
value above which Dendrogram attempt to identify the structures. The second
parameter min_delta is the minimum step required for a structure identified.
The third parameter min_npix is the minimum number of pixels that a structure
should contain in order to remain an independent structure. We set the three
parameters as follows: min_value $=5\sigma$, min_delta=$3\sigma$, and min_npix
= 100, where $\sigma$ is the average rms noise level of the 12CO data.We
select min_npix=100 which is in the middle of 4x4x4=64 and 5x5x5=125 voxels in
position-position-velocity space in order to remove artificial structure in
the area having high noise after some trials. The map angular resolution is
close to about 4 times the cell size. We keep only leaf structures, which are
independent structures in our analysis. This extraction with Dendrogram
results in the identification of 26 individual clumps in M17-HII and 164
clumps in M17-IRDC. The properties of these clumps can be found in Table 3.
Here, we assumed all the distances to the structures identified are equal to
the representative distance of $d=2$ kpc.

We use the virial parameters as the ratio of the virial mass to the true clump
mass, ‘$\alpha_{\rm vir}$’, as a measure of the gravitational stability of the
clumps. For clumps in gravitational equilibrium, $\alpha_{\rm vir}$ is unity.
Collapsing and dispersing clumps have $\alpha_{\rm vir}$ of $<1$ and $>1$,
respectively. The virial parameter for a spherical clump with uniform density
and temperature neglecting external pressure and magnetic field can be
expressed as

$\alpha_{\rm vir}=\frac{M_{\rm vir}}{M_{\rm clump}}=\frac{5\sigma_{\rm
3D}^{2}R}{3GM_{\rm clump}}\,,$ (7)

where $G$, $M$ and $R$ are the gravitational constant, clump mass and clump
radius, respectively (Bertoldi & McKee, 1992). The clump radius $R$ is defined
as the geometric mean of the major and minor semi-axes of the projection onto
the position-position plane for a clump identified. 3D velocity dispersion
$\sigma_{\rm 3D}$ is calculated as $\sigma_{\rm 3D}=\sigma_{\rm 1D}/\sqrt{3}$.
The clump mass is the LTE mass calculated from the 12CO ($J=1-0$) integrated
intensity of the individual structures.

The masses of the individual clumps extracted with Dendrogram in M17-HII
appear to be comparable to those in M17-IRDC while the peak column density in
M17-HII is higher than in M17-IRDC as seen in the histogram of column density
(Figure 8). The median clump mass is 60 and 14 $\rm M_{\sun}$ in M17-HII and
M17-IRDC, respectively. Both clumps in M17-HII and M17-IRDC have a median
radius of 0.2 pc. The virial parameters of clumps in M17-HII are smaller than
those in M17-IRDC. 64% of clumps in M17-IRDC has virial parameteres
$\alpha_{\rm vir}>1$ while 42% of clumps in M17-HII has $\alpha_{\rm vir}>1$.
However, the median virial parameters in M17-IRDC is 1.36, higher than the
median value 0.86 in M17-HII. Thus, the clumps in M17-HII are more prone to
gravitational contraction, which is consistent with the fact that star
formation is much more active in M17-HII.

###  4.5. Distribution of dense gas traced by HCO+ and HCN

Figure 9.— (a) The HCO+ ($J=1-0$) and (b) HCN ($J=1-0$) integrated intensity
maps of M17. The red rectangles outline the two prominent star-forming
regions: M17-HII (left) and M17-IRDC (right). The star symbols pinpoint the OB
star clusters responsible for the giant HII region. The cross symbols pinpoint
the locations of the massive cores (M $>$ 500 $\rm M_{\sun}$) reported by
Shimoikura et al. (2019). White contours correspond to the gas column density
level of $3\times 10^{22}\,\mbox{cm${}^{-2}$}$.

M17-HII and M17-IRDC have different high column density gas concentration seen
in column density maps created from CO ($J=1-0$) observations as discussed in
Section 4.2. We use HCO+ ($J=1-0$) and HCN ($J=1-0$) to further examine the
distribution of high density gas in M17, which are thought to be better
tracers of dense star-forming gas than CO isotopologues (Gao & Solomon, 2004;
Wu et al., 2010; Stephens et al., 2016). The optically thin critical densities
of HCO+ ($J=1-0$) is as high as $6.8\times 10^{4}$ cm-3 and that of HCN
($J=1-0$) is as high as $4.7\times 10^{5}$ cm-3 at 10 K (Shirley, 2015).
However, the effective critical densities at the same temperatures are
$9.5\times 10^{2}$ cm-3 for HCO+ ($J=1-0$) and $8.4\times 10^{3}$ cm-3 for
HCN+ ($J=1-0$). For comparision, the critical density of 12 CO ($J=1-0$) is
less than 103 cm-3 (Shirley, 2015). Beside tracing the general landscape of
dense gas, these tracers are sensitive to the physical processes closely
related to star formation such as outflows, infall, photoionization,
mechanical energy, and chemistry (Sanhueza et al., 2012; Walker-Smith et al.,
2014; Chira et al., 2014; Fuller et al., 2005; Roberts et al., 2011; Shimajiri
et al., 2017).

In Figure 9, we present the integrated intensity maps of HCN and HCO+
($J=1-0$) transitions from 10 to 30  km s-1. The $1\sigma$ levels are $\sim
1\,\mbox{\,K}\mbox{\,km$\,$s${}^{-1}$}$ and
$1.2\,\mbox{\,K}\mbox{\,km$\,$s${}^{-1}$}$ for the HCN and HCO+ integrated
maps. It clearly shows that most of the emission of these lines is in M17-HII
region, indicating that M17-HII region contains a significant amount of high
density gas. The HCO+ and HCN emission come mainly from the high column
density ($>3\times 10^{22}\,\mbox{cm${}^{-2}$}$ or 600 $\rm M_{\sun}$pc-2)
part traced in CO except near the star cluster where the column density is
lower than $3\times 10^{22}\,\mbox{cm${}^{-2}$}$ but a small fraction of HCO+
and HCN ($J=1-0$) is still detected. In contrast, the emission of HCO+ and HCN
in M17-IRDC is almost invisible except in a few dense clumps and intersections
of the IRDC filamentary network (Busquet et al., 2016), indicating that there
are more dense gas at the intersections of the filaments (Ohashi et al.,
2016). These locations coincide with the bright 13CO emission positions (see
Figure 19). Note that these areas of both HCN and HCO+ emission are similar
and are the sums of all pixels that have HCN and HCO+ emission larger than
zero. The total areas of HCN/HCO+ of HII and M17-IRDC are 23.6 pc2 and 52.6
pc2, respectively. Thus the HCN and HCO+ emitting areas are 6.5% and 8.9% of
the total CO emitting areas for HII and M17-IRDC, respectively. While the
trends are similar, HCN integrated intensity is higher than that of HCO+. The
maximum HCO+ integrated intensity in M17-IRDC is about 5 K  km s-1 while that
of M17-HII goes up to 35 K  km s-1.

$\begin{array}[]{cc}\includegraphics[width=241.84842pt]{densetracer_o_nh2_m17hii.eps}&\includegraphics[width=256.0748pt]{densetracer_o_nh2_m17irdc.eps}\\\
\\\ \end{array}$

Figure 10.—  The scatter plots of pixel-by-pixel HCN and HCO+ ($J=1-0$)
integrated intensity versus gas column density for (a) M17-HII and (b)
M17-IRDC regions. We note that the sacle are different in these plots.

While the integrated intensities of HCN and HCO+ depend linearly on the gas
column density in M17-HII cloud, they do not exhibit similar correlation in
M17-IRDC. The Spearman correlation coefficients of HCN and HCO+ versus gas
column density are 0.72 and 0.68 in M17-HII cloud, and they are both 0.54 in
M17-IRDC. Similar trend is also found between the integrated intensities of
HCN and HCO+ and excitation temperature except that the linear dependence of
the integrated intensities of HCN and HCO+ only appear at temperature higher
than 50 K. The Spearman correlation coefficients of HCN and HCO+ versus
excitation temperature are lower: 0.65 and 0.59 in M17-HII cloud, and they are
0.49 and 0.39 in M17-IRDC, respectively. The mean ratios of HCO+/HCN are $\sim
0.73$ in M17-HII and $\sim 0.97$ in M17-IRDC. While the mean ratio in M17-HII
is similar to values obtained for resolved nearby galaxies in a recent survey
(Jiménez-Donaire et al., 2019), that of M17-IRDC is higher. Compare to
Galactic Cloud, the ratio of of HCO+/HCN in M17-HII is comparable to Ophiuchus
(0.77) and higher than Aquila (0.63) but lower than Orion B (from 0.9 to 1.4)
while the value of M17-IRDC is more comparable to those of Orion B (Shimajiri
et al., 2017).

$\begin{array}[]{cc}\includegraphics[width=241.84842pt]{densetracer_o_T_m17hii.eps}&\includegraphics[width=256.0748pt]{densetracer_o_T_m17irdc.eps}\\\
\\\ \end{array}$

Figure 11.— The scatter plots of pixel-by-pixel HCN and HCO+ ($J=1-0$)
integrated intensity versus gas temperature for (a) M17-HII and (b) M17-IRDC
regions. We note that the sacle are different in these plots.

##  5\. Discussion

In the previous Section, we have quantified the difference between M17-HII and
M17-IRDC in terms of the cloud mass distribution and the distribution of dense
gas tracers. In this Section, we will discuss the difference between two sub-
regions and how their distributions impact star formation in M17.

###  5.1. Two distinct sub-regions of in the M17 cloud complex

M17-HII and M17-IRDC clouds have different physical properties as shown in
Section 4 and are also known for having different star formation activity
(Povich & Whitney, 2010; Povich et al., 2016). We summarize the properties of
the two sub-regions in Figure 12. M17-HII is habouring a massive protostellar
cluster (Kuhn et al., 2015) while M17-IRDC is quiescient and currently forming
low-mass stars (Ohashi et al., 2016). M17-HII is warmer and has higher peak
column density and much higher very dense gas fraction. M17-IRDC is more
massive and has higher mean surface density. The total fraction of
$\frac{L_{HCN\,J=1-0}}{L_{HCO^{+}\,J=1-0}}$ and fraction of dense gas area
over CO gas area is also higher in M17-HII (see Section 5.2).

$\begin{array}[]{c}\includegraphics[width=512.1496pt]{subregions_compare.eps}\\\
\end{array}$

Figure 12.— A comparision of different physical properties of the M17-HII and
M17-IRDC clouds: maximum excitation temperature $T_{\rm max}$, maximum column
density $N_{\rm H_{2},max}$, very dense gas ($N_{\rm H_{2}}>1\times 10^{23}$
cm-2) ratio $f_{\rm verydense}$, total mass $M_{\rm tot}$, mean gas surface
density $\Sigma_{\rm mean}$, HCN $J=1-0$ line luminosity, HCO+ $J=1-0$ line
luminosity, HCN $J=1-0$/HCO+ $J=1-0$ line luminosity ratio, ratio of dense gas
area over total gas area $A_{HCNorHCO^{+}}/A_{CO}$.

###  5.2. HCO+ and HCN (J = 1–0) as proxies to trace dense gas mass

$\begin{array}[]{cc}\includegraphics[width=241.84842pt]{cummulativeLHCN.eps}&\includegraphics[width=241.84842pt]{cummulativeLHCOp.eps}\\\
\\\ \end{array}$

Figure 13.— The HCN (a) and HCO+ ($J=1-0$) (b) normalized cumulative
luminosity as compared to the gas column density CMD.

The distributions of HCO+ and HCN ($J=1-0$) coincide with the high density
part of the gas column density map, suggesting that HCO+ and HCN ($J=1-0$)
emission lines trace the similar density region. As in Section 4.3, we divide
the integrated intensity maps of HCN and HCO+ ($J=1-0$) into 120 bins based on
the gas column density bins ranging from $10^{21}$ cm-2 to $10^{24}$ cm-2. We
then calculate the line luminosity function (LLF) in the formed of the
normalized cumulative line luminosity function (nCLLF) as:

${\rm LLF(>N_{\rm H_{2}})}=\frac{\int_{N_{\rm H_{2}}}^{N_{\rm
H_{2},max}}A({N_{\rm H_{2}}})dL_{\rm line}(N_{\rm H_{2}})}{\int_{0}^{N_{\rm
H_{2},max}}A({N_{\rm H_{2}}})dL_{\rm line}(N_{\rm H_{2}})}\ .$ (8)

We use the same approach detailed in Solomon et al. (1997) or Wu et al. (2010)
to calculate the line luminosity as

$L_{\rm line}=23.5\Omega_{s*b}D_{\rm L}^{2}I_{\rm line}\left(1+z\right)^{-3}$
(9)

where $\Omega_{s*b}$ is the source solid angle convolved with the telescope
beam and has a unit of arcsec2. $D_{\rm L}$ is the luminosity distance and has
a unit of Mpc, $I_{\rm line}$ is the integrated line intensity and has a unit
of K  km s-1. z is the redshift of the object. The luminosity is expressed in
the unit of K km s-1pc2. If the source is much smaller than the beam, then
$\Omega_{s*b}$ is equal to the beam size $\Omega_{b}$. In the case of Galactic
clumps and clouds, the sources are often more extended than the beam, the
source solid angle convolved with a Gaussian beam is expressed as

$\Omega_{s*b}=\left(\frac{\pi\times\theta_{s}^{2}}{4ln2}\right)\left(\frac{\theta_{s}^{2}+\theta_{\rm
beam}^{2}}{\theta_{s}^{2}}\right).$ (10)

Therefore, the line luminosity in unit of K km s-1pc2 of an object in the
Milky Way ($z=0$) can be derived as

$L_{\rm line}=23.5\times 10^{-6}\Omega_{s*b}d^{2}I_{\rm line}$ (11)

where $d$ is distance and has a unit of kpc and $T_{\rm MB}$ is the main-beam
brightness temperature of the line. The line luminosity can be converted from
K km s-1pc2 to solar bolometric luminosity unit as in Nguyen-Luong et al.
(2013). The line luminosity function-$N_{\rm H_{2}}$ profiles are shown in
Figure 13 and are also compared with DGMFs.

The luminosity functions of both lines span the entire column density range
traced by the gas column density and are shallower than the DGMF profiles.
Therefore, both HCO+ and HCN ($J=1-0$) integrated intensities can serve as
proxies for high column density dense gas tracers, at least at the scale
smaller than cloud scale. In contrast, other works have shown that HCN can
trace more diffuse gas than HCO+ (i.e. Kauffmann et al. 2017; Pety et al.
2017; Shimajiri et al. 2017). The difference comes from the fact that M17 is a
massive star-forming regions while the molecular clouds in other studies are
low-mass forming regions.

While the luminosity functions of HCO+ and HCN are similar in M17-IRDC, the
HCN luminosity function is higher than that of HCO+ in M17-HII. Therefore, the
ratio of HCN/HCO+ in M17-IRDC is lower than the ratio in M17-HII. Lower
HCN/HCO+ might be related to the quiescent state of on-going state of the IRDC
while the higher ratio of HCN/HCO+ might reflect the cloud at a more advanced
state as free electrons in the turbulent and Far-UV irradiated environment
near the massive cluster can easily recombined with HCO+ (Papadopoulos, 2007).
It agrees with the fact that HCN is enhanced in X-Ray dominated environment
that is produced by young massive stars in M17-HII. Actually, Meijerink &
Spaans (2005) and Meijerink et al. (2007) show that a lower ratio is observed
on the surface of the X-ray dominated region or photon-dominated region and a
higher ratio is often seen in high density and cold regions. Therefore, a high
HCN/HCO+ ratio might be a good indicator of massive star formation or more
evolved evolutionary stages of star formation (see also Wu et al. 2010;
Sanhueza et al. 2012).

###  5.3. Stability of clumps in M17-IRDC and M17-HII

$\begin{array}[]{c}\includegraphics[width=483.69684pt]{sig_vir_scatter_logb.eps}\\\
\end{array}$

Figure 14.— Virial parameters as functions of (a) mass and (b) radius of
individual clumpss in M17-HII (red) and M17-IRDC regions (green). Virial
parameters of N2H+ (J = 1–0) cores (blue) derived from NRO 45m observations
(Shimoikura et al., 2019) and N2H+ (J = 1–0) dense cores (grey) derived from
ALMA observations (Ohashi et al., 2016) are also plotted, respectively.

There are more unbound clumps in M17-IRDC region than in M17-HII region, as
seen in the $\alpha_{\rm vir}-M$ and $\alpha_{\rm vir}-R$ relations in a log
scale (Figure 14). 64% of the clumps in M17-IRDC have $\alpha_{vir}>1$. In
addition, the clumps in M17-IRDC have systematically higher virial parameters
than those in M17-HII. These facts support the idea that the clumps in M17-HII
are more prone to gravitational contraction while those in M17-IRDC that have
$\alpha_{vir}>1$ are gravitationally unbound and dispersing. However, there is
a chance that they can be in the gravitational equilibrium if surrounded by
the high external pressure (see Shimoikura et al. 2019).

$\begin{array}[]{c}\includegraphics[width=227.62204pt]{stability.eps}\\\
\end{array}$

Figure 15.— The linewidth-size scaling coeffient and mass surface density
relation. The blue straight line show the case where there is no external
pressure. The orange, green, red, pink curves show the cases where extermal
pressure $P_{\rm e}/k$ are 104, 105, 106, and 107 K cm-3, respectively.

We compare the virial parameters of clumps in our studies with those of N2H+
(J = 1–0) cores (radius $\sim 0.1-1$ pc ) obtained from observation also with
NRO 45m (Shimoikura et al., 2019) and N2H+ (J = 1–0) dense cores (radius $\sim
0.007-0.03$ pc ) obtained from observation with the ALMA interferometer
(Ohashi et al., 2016). For the first dataset, we derive the 3D virial
parameter using their virial masses and core masses by applying Equation 7.
For the second one, because their virial parameters is calculated as
$\frac{5\sigma^{2}R}{GM}$, we divide it by 3 to obtain the 3D virial
parameters. While all ALMA dense cores have $\alpha_{\rm vir}<1$, most of NRO
45m cores have $\alpha_{\rm vir}<1$ and some cores have $\alpha_{\rm vir}>1$.
The trend is consistent with the general suggestion that virial parameter
increase with size (Ohashi et al., 2016; Chen et al., 2019). We suggest that
virial parameter increase with size until the core size reach $\sim 0.5-1$ pc
and decrease with size starting at the clump scales. We note that the 3D
virial parameters of molecular cloud complex (radius $\sim 50-100$ pc) are in
the range of 0.5–3 (Nguyen-Luong et al., 2016).

We also examine the significance of external pressures by plotting the
relationship between $\sigma_{\rm 3D}^{2}/R$ and the mass surface density
$\Sigma_{gas}$ in Figure 15 as theoretically suggested by Field et al. (2011)
as:

$\frac{\sigma_{\rm 3D}^{2}}{R}=\frac{1}{3}\left(\pi\Gamma
G\Sigma+\frac{4P_{\rm e}}{\Sigma}\right)$ (12)

where $\Gamma$ is a form factor which equals to 0.73 for an isothermal sphere
of critical mass with a centrally concentration internal density structure
(Elmegreen, 1989). $P_{\rm e}$ is the external pressure and is often expressed
as $\frac{P_{\rm e}}{k}$ in unit of K $\rm cm^{-3}$.

When $P_{\rm e}$ equals 0, the cloud is in simple virial equilibrium (SVE)
with the internal kinetic energy being equal to half the gravitational energy
(Field et al., 2011). In Figure 15, we overlaid the simple virial equilibrium
line in addition to some curves with external pressure ranging from
$10^{3}-10^{7}\,\rm K\,cm^{-3}$. Contrary to the clumps in Heyer et al. (2009)
that mostly lie above the SVE line, our data show that half of the clouds are
above and half are below the SVE line. The first half is dynamically unstable
due to external pressure while the second half is gravitationally bound and
collapses on its own gravity. This is an interesting fact since most of the
high resolution and high density cores (Ohashi et al., 2016; Shimoikura et
al., 2019) can collapse on its own while our CO clumps need external pressure
to collapse. Another interesting fact is that most clumps in M17-IRDC live
above the SVE line which mean they need additional external pressure to
collapse while those in M17-HII do not. This fact is consistent with the
higher virial parameters in M17-IRDC clumps and also consistent with what
found in molecular clouds in the Galactic Center (Miura et al., 2018).

###  5.4. Conditions for massive star formation in M17 HII and M17-IRDC

The mass functions of YSOs in the M17-HII and M17-IRDC regions were derived by
Povich & Whitney (2010) and Povich et al. (2016) using Spitzer data. They
found that the high-mass stellar population is deficient in M17-IRDC, claiming
a possibility that the massive star formation is delayed in the M17-IRDC
region assuming that the final stellar IMF approaches to the Salpeter IMF.
Here, we attempt to elucidate whether the present physical conditions of M17
satisfy the criteria of massive star formation, based on our observational
results.

$\begin{array}[]{c}\includegraphics[width=526.37598pt]{massive_dens.eps}\\\
\\\ \end{array}$

Figure 16.— Gas column density maps and dense gas distribution. The coloured
scale image indicates the column density distribution derived from 13CO. The
white contour is at the column density of $\sim$ 1 g cm-2. Blue contours
represent the HCO+ integrated intensity and the green contours represent the
HCN integrated intensity, both at 3.5 K km/s level. The star symbols pinpoint
the OB star clusters responsible for the giant HII region. The cross symbols
pinpoint the locations of the massive cores (M $>$ 500 $\rm M_{\sun}$)
reported by Shimoikura et al. (2019).

Krumholz & McKee (2008) proposed the threshold column density as one condition
for massive star formation of $\Sigma>1$ g cm-2 or $\rm N_{\rm
H_{2}}>10^{23}\,{\rm cm}^{-2}$, beyond which molecular clouds could form high-
mass stars, avoiding further fragmentation to form lower-mass cores. As shown
in Section 4, the cumulative column density distributions derived from our CO
data (see Figure 8) clearly indicate that M17-HII region is denser than the
M17-IRDC. In addition, M17-HII region is more evolved in term of forming stars
and M17-IRDC region is more quiescent. Although the median column density and
total mass of M17-IRDC are larger than those of the M17-HII region, most of
the clumps identified in M17-IRDC have column densities lower than the
threshold of massive star formation. In contrast, M17-HII contains several
clumps having column densities comparable to or larger than the threshold. For
comparison, we show the dense region in M17 traced by HCO+ and HCN $J=(1-0)$
emission in Figure 16. Therefore, we conclude that the current mass
concentration in M17-IRDC is not enough to efficiently create high-mass stars.
However, there is a large mass reservoir and a filamentary network that can
feed the clumps and cores to grow up and in the future to form high-mass stars
(Ohashi et al., 2016). External pressure might be needed for the clumps in
M17-IRDC to collapse and form stars as shown in Section 5.3.

###  5.5. What can be the star formation scenario for the entire M17 cloud
complex?

In Section 4, we show that the dense gas fraction and the dynamical states of
the M17-HII cloud and clumps within it are different from those of M17-IRDC.
And yet, both regions were suggested to be connected (Shimoikura et al., 2019)
and are formed by colliding clouds (M17-HII: Nishimura et al. 2018 and
M17-IRDC: Sugitani et al. 2019). In addition, Sugitani et al. (2019) used the
the near-infrared polarization and CO ($J=3-2$) data to discover that the main
elongation axis of M17-IRDC is perpendicular to the global magnetic fields,
which are roughly perpendicular to the Galactic plane. Such a structural and
magnetic configuration might be consistent with the molecular cloud formation
by the Parker instability (Parker, 1966; Shibata et al., 1992). Since the
Parker instability is more unstable to the anti-symmetric mode, the field
lines tend to cross the Galactic plane. According to the linear stability
analysis of the gravitational instability of magnetized Galactic disks (Hanawa
et al., 1992), the gravitational instability is unstable only for the
symmetric mode (see also Nakamura et al. 1991). When the GMCs formed by the
Parker instability, large-scale colliding clouds are a natural outcome because
the clouds slide down along the field lines and accumulated in the valleys of
magnetic fields.

This mechanism is also consistent with the suggestion of Elmegreen et al.
(1979) who suggested that the Galactic spiral density wave passed through the
whole M17 region from north-east to south-west, triggering the star formation
in M17-HII. In observations, large-scale colliding flows on the large scale is
proven to be an effective way to convert atomic gas to molecular gas and from
diffuse gas to dense gas (Nguyen Luong et al., 2011a, b; Nguyen-Luong et al.,
2013; Motte et al., 2014). See also the review on massive star formation
(Motte et al., 2018). Sugitani et al. (2019) also pointed out that two gas
components with velocities of $\sim$20  km s-1(main) and $\sim$35  km
s-1(secondary) in M17-IRDC could collide. These components can form a broad
feature bridge, which is a collection of diffuse gas in that has system
velocity in between $\sim$20  km s-1(main) and $\sim$35  km s-1(Sugitani et
al. 2019, Kinoshita et al. in prep.) that is a signature of cloud-cloud-
collision as suggested by numerical simulation of gas kinematics (Haworth et
al., 2015a, b; Inoue & Fukui, 2013) and by gas kinematic observations (Torii
et al., 2011, 2015, 2017, 2018a, 2018b; Fukui et al., 2014, 2016, 2018a,
2018b; Fujita et al., 2019; Tsuboi et al., 2015).

##  6\. Conclusion

We examined the molecular gas structure of the M17 complex using 12CO
($J=1-0$), 13CO ($J=1-0$), HCO+ ($J=1-0$), HCN ($J=1-0$) and other supplement
data. Our main findings are summarized as follows:

  1. 1.

There are three main molecular clouds with systemic velocities $\sim$ 20  km
s-1, 38  km s-1, and 57  km s-1 along the line-of-sight of M17 complex. The
main component of 20  km s-1 sits in the Sagittarius arm at a distance of
$\sim$ 2 kpc. The 38  km s-1 and 57  km s-1 clouds might be associated with
Scutum and Norma arms, respectively.

  2. 2.

The cloud complex can be divided into two clouds: M17-HII region containing
the NGC 6618 star cluster and M17-IRDC region containing the prominent IRDC
network. We confirmed from the molecular observations that M17-HII has a
significant fraction of molecular gas (27%) with high column densities and
high volume densities that surpasses massive star formation threshold of 1 g
cm-2, whereas such high column densities and high volume densities gas is
deficient in M17-IRDC (only 0.46%).

  3. 3.

M17-HII has a higher dense gas fraction than M17-IRDC as seen in the
Cumulative Mass Distributions of gas column density and the normalized
cumulative line luminosity of dense gas tracers such as HCN and HCO+
($J=1-0$). Observations of HCO+ and HCN emission also confirmed that more
denser gas presents in M17-II region than in M17-IRDC.

  4. 4.

HCO+ and HCN emission trace all gas with column density higher than $3\times
10^{22}$ cm-2 and higher HCN/HCO+ ratio in M17-II region might be a good
indicator of massive star formation or more evolved evolutionary stages of
star formation in M17-II.

  5. 5.

Applying a Dendrogram analysis to the 12CO data, we identified clumps in the
two clouds. Clumps in M17-HII are more compact than those in M17-IRDC and most
clumps have virial parameters $\alpha_{\rm vir}<=1$. On the other hand, most
clumps in M17-IRDC have virial parameters $\alpha_{\rm vir}>1$. Clumps in
M17-IRDC need external pressure while clumps in 17-HII can collapse under
their own gravity. Such distinct dynamical states of clumps is consistent with
the current activity of star formation where M17-HII is forming massive stars
more efficient in the past while intense massive star formation is not
happening yet in M17-IRDC.

  6. 6.

M17 complex appears to have been formed as a whole by large scale compression.
This compression triggered star formation in M17-HII and would also trigger
star formation in M17-IRDC in the future.

A part of this work was financially supported by JSPS KAKENHI Grant Numbers
JP17H02863, JP17H01118, JP26287030, and JP17K00963. The 45-m radio telescope
is operated by NRO, a branch of NAOJ.

## References

  * Ando et al. (2002) Ando, M., Nagata, T., Sato, S., et al. 2002, ApJ, 574, 187
  * André et al. (2010) André, P., Men’shchikov, A., Bontemps, S., et al. 2010, A&A, 518, L102+
  * Arzoumanian et al. (2011) Arzoumanian, D., André, P., Didelon, P., et al. 2011, A&A, 529, L6+
  * Barnes et al. (2015) Barnes, P. J., Muller, E., Indermuehle, B., et al. 2015, ApJ, 812, 6
  * Bertoldi & McKee (1992) Bertoldi, F., & McKee, C. F. 1992, ApJ, 395, 140
  * Bolatto et al. (2013) Bolatto, A. D., Wolfire, M., & Leroy, A. K. 2013, ARA&A, 51, 207
  * Busquet et al. (2016) Busquet, G., Estalella, R., Palau, A., et al. 2016, ApJ, 819, 139
  * Carlhoff et al. (2013) Carlhoff, P., Nguyen Luong, Q., Schilke, P., et al. 2013, A&A, 560, A24
  * Chen et al. (2019) Chen, H.-R. V., Zhang, Q., Wright, M. C. H., et al. 2019, ApJ, 875, 24
  * Chibueze et al. (2016) Chibueze, J. O., Kamezaki, T., Omodaka, T., et al. 2016, MNRAS, 460, 1839
  * Chini et al. (1980) Chini, R., Elsaesser, H., & Neckel, T. 1980, A&A, 91, 186
  * Chira et al. (2014) Chira, R.-A., Smith, R. J., Klessen, R. S., Stutz, A. M., & Shetty, R. 2014, MNRAS, 444, 874
  * Csengeri et al. (2017) Csengeri, T., Bontemps, S., Wyrowski, F., et al. 2017, A&A, 601, A60
  * Dame et al. (2001) Dame, T. M., Hartmann, D., & Thaddeus, P. 2001, ApJ, 547, 792
  * Dempsey et al. (2013) Dempsey, J. T., Thomas, H. S., & Currie, M. J. 2013, ApJS, 209, 8
  * Dickman (1978) Dickman, R. L. 1978, ApJS, 37, 407
  * Dobashi et al. (2019a) Dobashi, K., Shimoikura, T., Endo, N., et al. 2019a, PASJ, 71, S11
  * Dobashi et al. (2019b) Dobashi, K., Shimoikura, T., Katakura, S., Nakamura, F., & Shimajiri, Y. 2019b, PASJ, 58
  * Elmegreen (1989) Elmegreen, B. G. 1989, ApJ, 338, 178
  * Elmegreen et al. (1979) Elmegreen, B. G., Lada, C. J., & Dickinson, D. F. 1979, ApJ, 230, 415
  * Evans et al. (2014) Evans, II, N. J., Heiderman, A., & Vutisalchavakul, N. 2014, ApJ, 782, 114
  * Field et al. (2011) Field, G. B., Blackman, E. G., & Keto, E. R. 2011, MNRAS, 416, 710
  * Fujita et al. (2019) Fujita, S., Torii, K., Kuno, N., et al. 2019, PASJ, 46
  * Fukui et al. (2014) Fukui, Y., Ohama, A., Hanaoka, N., et al. 2014, ApJ, 780, 36
  * Fukui et al. (2016) Fukui, Y., Torii, K., Ohama, A., et al. 2016, ApJ, 820, 26
  * Fukui et al. (2018a) Fukui, Y., Torii, K., Hattori, Y., et al. 2018a, ApJ, 859, 166
  * Fukui et al. (2018b) Fukui, Y., Ohama, A., Kohno, M., et al. 2018b, PASJ, 70, S46
  * Fuller et al. (2005) Fuller, G. A., Williams, S. J., & Sridharan, T. K. 2005, A&A, 442, 949
  * Gao & Solomon (2004) Gao, Y., & Solomon, P. M. 2004, ApJS, 152, 63
  * Hanawa et al. (1992) Hanawa, T., Matsumoto, R., & Shibata, K. 1992, ApJ, 393, L71
  * Hanson et al. (1997) Hanson, M. M., Howarth, I. D., & Conti, P. S. 1997, ApJ, 489, 698
  * Haworth et al. (2015a) Haworth, T. J., Shima, K., Tasker, E. J., et al. 2015a, MNRAS, 454, 1634
  * Haworth et al. (2015b) Haworth, T. J., Tasker, E. J., Fukui, Y., et al. 2015b, MNRAS, 450, 10
  * Heiderman et al. (2010) Heiderman, A., Evans, II, N. J., Allen, L. E., Huard, T., & Heyer, M. 2010, ApJ, 723, 1019
  * Henkel et al. (1982) Henkel, C., Wilson, T. L., & Bieging, J. 1982, A&A, 109, 344
  * Heyer et al. (2009) Heyer, M., Krawczyk, C., Duval, J., & Jackson, J. M. 2009, ApJ, 699, 1092
  * Honma et al. (2012) Honma, M., Nagayama, T., Ando, K., et al. 2012, PASJ, 64, 136
  * Inoue & Fukui (2013) Inoue, T., & Fukui, Y. 2013, ApJ, 774, L31
  * Ishii et al. (2019) Ishii, S., Nakamura, F., Shimajiri, Y., et al. 2019, PASJ, 87
  * Jiménez-Donaire et al. (2019) Jiménez-Donaire, M. J., Bigiel, F., Leroy, A. K., et al. 2019, ApJ, 880, 127
  * Kainulainen et al. (2013) Kainulainen, J., Federrath, C., & Henning, T. 2013, A&A, 553, L8
  * Kauffmann et al. (2017) Kauffmann, J., Goldsmith, P. F., Melnick, G., et al. 2017, A&A, 605, L5
  * Krumholz & McKee (2008) Krumholz, M. R., & McKee, C. F. 2008, Nature, 451, 1082
  * Kuhn et al. (2015) Kuhn, M. A., Getman, K. V., & Feigelson, E. D. 2015, ApJ, 802, 60
  * Kusune et al. (2019) Kusune, T., Nakamura, F., Sugitani, K., et al. 2019, PASJ, 57
  * Lacy et al. (2017) Lacy, J. H., Sneden, C., Kim, H., & Jaffe, D. T. 2017, ApJ, 838, 66
  * Lada et al. (2010) Lada, C. J., Lombardi, M., & Alves, J. F. 2010, ApJ, 724, 687
  * Lada et al. (1991) Lada, E. A., Bally, J., & Stark, A. A. 1991, ApJ, 368, 432
  * Langer & Penzias (1990) Langer, W. D., & Penzias, A. A. 1990, ApJ, 357, 477
  * Mangum & Shirley (2015) Mangum, J. G., & Shirley, Y. L. 2015, PASP, 127, 266
  * Meidt et al. (2013) Meidt, S. E., Schinnerer, E., García-Burillo, S., et al. 2013, ApJ, 779, 45
  * Meijerink & Spaans (2005) Meijerink, R., & Spaans, M. 2005, A&A, 436, 397
  * Meijerink et al. (2007) Meijerink, R., Spaans, M., & Israel, F. P. 2007, A&A, 461, 793
  * Minamidani et al. (2016) Minamidani, T., Nishimura, A., Miyamoto, Y., et al. 2016, in Proc. SPIE, Vol. 9914, Millimeter, Submillimeter, and Far-Infrared Detectors and Instrumentation for Astronomy VIII, 99141Z
  * Miura et al. (2018) Miura, R. E., Espada, D., Hirota, A., et al. 2018, ApJ, 864, 120
  * Motte et al. (2018) Motte, F., Bontemps, S., & Louvet, F. 2018, ARA&A, 56, 41
  * Motte et al. (2014) Motte, F., Nguyên Luong, Q., Schneider, N., et al. 2014, A&A, 571, A32
  * Nakamura et al. (1991) Nakamura, F., Hanawa, T., & Nakano, T. 1991, PASJ, 43, 685
  * Nakamura et al. (2012) Nakamura, F., Miura, T., Kitamura, Y., et al. 2012, ApJ, 746, 25
  * Nakamura et al. (2019a) Nakamura, F., Oyamada, S., Okumura, S., et al. 2019a, PASJ, 32
  * Nakamura et al. (2019b) Nakamura, F., Ishii, S., Dobashi, K., et al. 2019b, PASJ, 71, S3
  * Nguyen Luong et al. (2011a) Nguyen Luong, Q., Motte, F., Hennemann, M., et al. 2011a, A&A, 535, A76
  * Nguyen Luong et al. (2011b) Nguyen Luong, Q., Motte, F., Schuller, F., et al. 2011b, A&A, 529, A41+
  * Nguyen-Luong et al. (2013) Nguyen-Luong, Q., Motte, F., Carlhoff, P., et al. 2013, ApJ, 775, 88
  * Nguyen-Luong et al. (2016) Nguyen-Luong, Q., Nguyen, H. V. V., Motte, F., et al. 2016, ApJ, 833, 23
  * Nielbock et al. (2001) Nielbock, M., Chini, R., Jütte, M., & Manthey, E. 2001, A&A, 377, 273
  * Nishimura et al. (2018) Nishimura, A., Minamidani, T., Umemoto, T., et al. 2018, PASJ, 70, S42
  * Ohashi et al. (2016) Ohashi, S., Sanhueza, P., Chen, H.-R. V., et al. 2016, ApJ, 833, 209
  * Papadopoulos (2007) Papadopoulos, P. P. 2007, ApJ, 656, 792
  * Parker (1966) Parker, E. N. 1966, ApJ, 145, 811
  * Pety et al. (2017) Pety, J., Guzmán, V. V., Orkisz, J. H., et al. 2017, A&A, 599, A98
  * Povich et al. (2016) Povich, M. S., Townsley, L. K., Robitaille, T. P., et al. 2016, ApJ, 825, 125
  * Povich & Whitney (2010) Povich, M. S., & Whitney, B. A. 2010, ApJ, 714, L285
  * Rainey et al. (1987) Rainey, R., White, G. J., Gatley, I., et al. 1987, A&A, 171, 252
  * Reid et al. (2016) Reid, M. J., Dame, T. M., Menten, K. M., & Brunthaler, A. 2016, ApJ, 823, 77
  * Reid et al. (2019) Reid, M. J., Menten, K. M., Brunthaler, A., et al. 2019, ApJ, 885, 131
  * Rivera-Ingraham et al. (2015) Rivera-Ingraham, A., Martin, P. G., Polychroni, D., et al. 2015, ApJ, 809, 81
  * Roberts et al. (2011) Roberts, H., van der Tak, F. F. S., Fuller, G. A., Plume, R., & Bayet, E. 2011, A&A, 525, A107
  * Rosolowsky et al. (2008) Rosolowsky, E. W., Pineda, J. E., Kauffmann, J., & Goodman, A. A. 2008, ApJ, 679, 1338
  * Sanhueza et al. (2012) Sanhueza, P., Jackson, J. M., Foster, J. B., et al. 2012, ApJ, 756, 60
  * Schneider et al. (2010) Schneider, N., Csengeri, T., Bontemps, S., et al. 2010, A&A, 520, A49
  * Schuller et al. (2010) Schuller, F., Beuther, H., Bontemps, S., et al. 2010, The Messenger, 141, 20
  * Scoville et al. (1986) Scoville, N. Z., Sanders, D. B., & Clemens, D. P. 1986, ApJ, 310, L77
  * Shibata et al. (1992) Shibata, K., Nozawa, S., & Matsumoto, R. 1992, PASJ, 44, 265
  * Shimajiri et al. (2017) Shimajiri, Y., André, P., Braine, J., et al. 2017, A&A, 604, A74
  * Shimoikura et al. (2019) Shimoikura, T., Dobashi, K., Hirose, A., et al. 2019, PASJ, 82
  * Shimoikura et al. (2018) Shimoikura, T., Dobashi, K., Nakamura, F., Shimajiri, Y., & Sugitani, K. 2018, PASJ, 131
  * Shirley (2015) Shirley, Y. L. 2015, PASP, 127, 299
  * Solomon et al. (1997) Solomon, P. M., Downes, D., Radford, S. J. E., & Barrett, J. W. 1997, ApJ, 478, 144
  * Stephens et al. (2016) Stephens, I. W., Jackson, J. M., Whitaker, J. S., et al. 2016, ApJ, 824, 29
  * Sugitani et al. (2019) Sugitani, K., Nakamura, F., Shimoikura, T., et al. 2019, PASJ, 89
  * Tan (2000) Tan, J. C. 2000, ApJ, 536, 173
  * Tanabe et al. (2019) Tanabe, Y., Nakamura, F., Tsukagoshi, T., et al. 2019, arXiv e-prints, arXiv:1910.07495
  * Torii et al. (2011) Torii, K., Enokiya, R., Sano, H., et al. 2011, ApJ, 738, 46
  * Torii et al. (2015) Torii, K., Hasegawa, K., Hattori, Y., et al. 2015, ApJ, 806, 7
  * Torii et al. (2017) Torii, K., Hattori, Y., Hasegawa, K., et al. 2017, ApJ, 835, 142
  * Torii et al. (2018a) Torii, K., Hattori, Y., Matsuo, M., et al. 2018a, PASJ, 121
  * Torii et al. (2018b) Torii, K., Fujita, S., Matsuo, M., et al. 2018b, PASJ, 70, S51
  * Townsley et al. (2003) Townsley, L. K., Feigelson, E. D., Montmerle, T., et al. 2003, ApJ, 593, 874
  * Tsuboi et al. (2015) Tsuboi, M., Miyazaki, A., & Uehara, K. 2015, PASJ, 67, 109
  * Urquhart et al. (2018) Urquhart, J. S., König, C., Giannetti, A., et al. 2018, MNRAS, 473, 1059
  * Walker-Smith et al. (2014) Walker-Smith, S. L., Richer, J. S., Buckle, J. V., Hatchell, J., & Drabek-Maunder, E. 2014, MNRAS, 440, 3568
  * White et al. (1982) White, G. J., Phillips, J. P., Beckman, J. E., & Cronin, N. J. 1982, MNRAS, 199, 375
  * Wienen et al. (2015) Wienen, M., Wyrowski, F., Menten, K. M., et al. 2015, A&A, 579, A91
  * Wilson et al. (1970) Wilson, R. W., Jefferts, K. B., & Penzias, A. A. 1970, ApJ, 161, L43
  * Wu et al. (2017) Wu, B., Tan, J. C., Christie, D., et al. 2017, ApJ, 841, 88
  * Wu et al. (2010) Wu, J., Evans, II, N. J., Shirley, Y. L., & Knez, C. 2010, ApJS, 188, 313
  * Wu et al. (2014) Wu, Y. W., Sato, M., Reid, M. J., et al. 2014, A&A, 566, A17
  * Wu et al. (2019) Wu, Y. W., Reid, M. J., Sakai, N., et al. 2019, ApJ, 874, 94
  * Xu et al. (2011) Xu, Y., Moscadelli, L., Reid, M. J., et al. 2011, ApJ, 733, 25

##  Appendix A Dendrogram masked map and results

Figure 17a shows the masked map used to extract sources with Dendrogram where
we masked out all pixels with column density below $3\times 10^{22}$ cm-2 and
Figure 17b shows the detected leaf structures extracted with Dendrogram.

Figure 17.— The masked map used to extract sources with Dendrogram where we
masked all pixels with column density below $3\times 10^{22}$ cm-2 (upper) and
the detected leaf structures extracted with the Dendrogram (lower).

##  Appendix B 12CO and 13CO ($J=1-0$) intensity maps of the clouds along the
line of sight of M17 complex integrated in different velocity ranges

We show the 12CO and 13CO ($J=1-0$) integrated intensities of the observed
region integrated over different velocity ranges in Figures 18 and 19,
respectively.

Figure 18.— 12CO ($J=1-0$) integrated intensity maps of the M17 complex. The
velocity range for integration is indicated in the upper-left corner of each
panel. The red rectangles outline the two prominent star-forming regions:
M17-HII (left) and M17-IRDC (right). The star symbols pinpoint the OB star
clusters responsible for the giant HII region. The cross symbols pinpoint the
locations of the massive cores (M $>$ 500 $\rm M_{\sun}$) reported by
Shimoikura et al. (2019).

Figure 19.— 13CO ($J=1-0$) integrated intensity maps of the M17 complex. The
velocity range for integration is indicated in the upper-left corner of each
panel. The red rectangles outline the two prominent star-forming regions:
M17-HII (left) and M17-IRDC (right). The star symbols pinpoint the OB star
clusters responsible for the giant HII region. The cross symbols pinpoint the
locations of the massive cores (M $>$ 500 $\rm M_{\sun}$) reported by
Shimoikura et al. (2019).