11institutetext: Instituto de Radioastronomía y Astrofísica, Universidad
Nacional Autónoma de México, Apdo. Postal 3-72, 58089 Morelia, Michoacán,
México 11email<EMAIL_ADDRESS>22institutetext: Univ. Grenoble Alpes,
CNRS, IPAG, 38000 Grenoble, France 33institutetext: AIM, CEA, CNRS, Université
Paris-Saclay, Université Paris Diderot, Sorbonne Paris Cité, 91191 Gif-sur-
Yvette, France 44institutetext: School of Physics and Astronomy, University of
Exeter, Stocker Road, Exeter, EX4 4QL, UK 55institutetext: Physikalisches
Institut, Universität zu Köln, Zülpicher Str. 77, 50937 Köln, Germany
66institutetext: School of Physics and Astronomy, University of Leeds, Leeds
LS2 9JT, U.K. 77institutetext: Laboratoire d’astrophysique de Bordeaux, Univ.
Bordeaux, CNRS, B18N, allée Geoffroy Saint-Hilaire, 33615 Pessac, France
88institutetext: INAF - Istituto di Astrofisica e Planetologia Spaziali
(IAPS), via Fosso del Cavaliere 100, 00133 Roma, Italy

# Mass segregation and sequential star formation in NGC 2264 revealed by
_Herschel_ ††thanks: _Herschel_ is an ESA space observatory with science
instruments provided by European-led Principal Investigator consortia and with
important participation from NASA.

T. Nony 11 J.-F. Robitaille 22 F. Motte 22 M. Gonzalez 22 I. Joncour 22 E.
Moraux 22 A. Men’shchikov 33 P. Didelon 33 F. Louvet 33 A. S. M. Buckner 44 N.
Schneider 55 S. L. Lumsden 66 S. Bontemps 77 Y. Pouteau 22 N. Cunningham 22 E.
Fiorellino 88 R. Oudmaijer 66 P. André 33 B. Thomasson 22

###### Abstract

Context. The mass segregation of stellar clusters could be primordial rather
than dynamical. Despite the abundance of studies of mass segregation for
stellar clusters, those for stellar progenitors are still scarce, so the
question on the origin and evolution of mass segregation is still open.

Aims. Our goal is to characterize the structure of the NGC 2264 molecular
cloud and compare the populations of clumps and young stellar objects (YSOs)
in this region whose rich YSO population has shown evidence of sequential star
formation.

Methods. We separated the _Herschel_ column density map of NGC 2264 in three
subregions and compared their cloud power spectra using a multiscale
segmentation technique. We extracted compact cloud fragments from the column
density image, measured their basic properties, and studied their spatial and
mass distributions.

Results. We identified in the whole NGC 2264 cloud a population of 256 clumps
with typical sizes of $\sim$0.1 pc and masses ranging from 0.08 $M_{\odot}$ to
53 $M_{\odot}$. Although clumps have been detected all over the cloud, the
central subregion of NGC 2264 concentrates most of the massive, bound clumps.
The local surface density and the mass segregation ratio indeed indicate a
strong degree of mass segregation for the 15 most massive clumps, with a
median $\Sigma_{6}$ three time that of the whole clumps population and
$\Lambda_{\rm MSR}\simeq 8$. We showed that this cluster of massive clumps is
forming within a high-density cloud ridge, itself formed and probably still
fed by the high concentration of gas observed on larger scales in the central
subregion. The time sequence obtained from the combined study of the clump and
YSO populations in NGC 2264 suggests that the star formation started in the
northern subregion, that it is now actively developing at the center and will
soon start in the southern subregion.

Conclusions.  Taken together, the cloud structure and the clump and YSO
populations in NGC 2264 argue for a dynamical scenario of star formation.
Cloud could first undergo global collapse, driving most clumps to centrally
concentrated ridges/hubs. After their main accretion phase, some, and probably
the most massive, YSOs would stay clustered while others would be ejected from
their birth sites. We propose that the mass segregation observed in some star
clusters could be inherited from that of clumps, originating from the mass
assembly phase of molecular clouds.

###### Key Words.:

ISM: structure - stars: formation - methods: statistical - open clusters and
associations: individual: NGC 2264

##  1 Introduction

Among the major open questions in the field of stellar cluster formation today
is to define whether stellar properties, and especially their clustering
characteristics and dynamical state, could be inherited from the properties of
the clouds themselves. Mass segregation has been studied extensively in
stellar clusters for decades, to investigate if the energy equipartition
associated with two body relaxation (Spitzer, 1969) causes high-mass stars to
‘fall’ toward the center of mass of clusters. As a matter of fact, mass
segregation has been observed, as expected, in dynamically old globular and
open clusters, but also in very young regions, suggesting it was not due to
dynamical evolution (see, e.g the review by Meylan, 2000, and references
therein). Primordial mass segregation prompts the question of whether there
are preferential sites for high-mass star formation (Murray & Lin, 1996) or
whether the observed segregation is related to energy equipartition at all
(e.g., Parker et al., 2016). Simulations indicate that primordial structure,
if present, can be rapidly erased through dynamical interactions of the stars
and expulsion of the gas (Parker & Meyer, 2012; Fujii & Portegies Zwart,
2016). Observational studies, however, show a weak, if any, correlation
between the substructure and the age of the stellar clusters (Sánchez &
Alfaro, 2009; Dib et al., 2018). Hetem & Gregorio-Hetem (2019) indicate that
stellar clusters did not significantly change in terms of structure within
their first 10 Myrs, and corroborate the absence of correlation between mass
segregation and the age of clusters, as initially reported by Dib et al.
(2018). Specifically, measuring the spatial distribution and mass segregation
of molecular cloud fragments and comparing it to that of very young stellar
objects is essential to study this link. A handful of studies have started
investigating the mass segregation of molecular cores (Plunkett et al., 2018;
Dib & Henning, 2019; Román-Zúñiga et al., 2019), mostly focusing, first, on
low-mass star-forming regions.

The _Herschel_ observatory imaged, with an unprecedented sensitivity and
angular resolution, the low- to high-mass star-forming clouds located at
100–500 pc and 0.7–3 kpc from the Sun (André et al., 2010; Motte et al.,
2010). These surveys provide the opportunity to build large, if not complete,
catalogs of cloud fragments with 0.01-0.1 pc (qualified of cores) to 0.1-1 pc
sizes (called clumps). At these scales, clumps will likely fragment into
cores, which could themselves be the direct progenitors of stars or small
stellar systems. HOBYS111see http://hobys-herschel.cea.fr, the _Herschel_
imaging survey of OB young stellar objects (Motte et al., 2010), is the first
systematic survey of a complete sample of nearby molecular cloud complexes
forming high-mass stars. The wide-field photometric imaging, performed with
both the SPIRE and PACS cameras of _Herschel_ , aims at completing the census
of high-mass star progenitors at 0.1 pc scales in essentially all the
molecular cloud complexes at less than 3 kpc. The HOBYS sample contains seven
massive, $3\times 10^{5}$ to $3\times 10^{6}$ $M_{\odot}$, molecular complexes
at 1.4-3 kpc from the Sun, including Cygnus X and NGC 6334, and two
intermediate-mass, a few $10^{5}$ $M_{\odot}$, cloud complexes at 0.7-0.8 kpc,
Vela C and Monoceros (Mon R1, Mon R2, NGC 2264).

HOBYS notably revealed a tight link between the density, dynamics, and clump
population of the so-called ridges (Motte et al., 2018). The latter are high-
density filaments ($n>10^{5}$ cm-3 over $\sim$5 pc3) forming clusters of high-
mass stars (e.g., Schneider et al., 2010; Hill et al., 2011; Nguyen Luong et
al., 2011; Hennemann et al., 2012; Nguyen Luong et al., 2013; Tigé et al.,
2017), whereas hubs are more spherical smaller structures forming at most a
couple of high-mass stars (e.g., Schneider et al., 2012; Peretto et al., 2013;
Rivera-Ingraham et al., 2013; Didelon et al., 2015; Rayner et al., 2017). The
existence of ridges and hubs is predicted by dynamical models of cloud
formation such as colliding flow or gravitationally-driven gas inflows
simulations (e.g., Heitsch et al., 2008; Smith et al., 2009; Hartmann et al.,
2012) and some analytical theories of filament collapse/conveyor belt (Myers,
2009; Krumholz & McKee, 2020).

The Monoceros OB1 (Mon OB1) cloud complex is located at a Gaia DR2 determined
distance of 723${}^{+56}_{-49}$ pc (Cantat-Gaudin et al., 2018). In the mid-
infrared, it displays a 2.5$\degree$ ($\sim$30 pc) diameter half loop
(Schwartz, 1987), of which three 10 pc clouds have been imaged by _Herschel_
as part of the HOBYS and Galactic Cold Cores key programs (Motte et al., 2010;
Juvela et al., 2012). NGC 2264 and G202.3+2.5 lie at the eastern extremity of
Mon OB1, Monoceros R1 is in the west. NGC 2264 is the best-studied cloud of
the Mon OB1 complex (see the review by Dahm, 2008). Over the past decade, a
population of more than 1000 Young Stellar Objects (YSOs) at various
evolutionary stages has been revealed (Teixeira et al., 2012; Povich et al.,
2013; Rapson et al., 2014; Venuti et al., 2018). The NGC 2264 cloud is
elongated in the NW-SE direction, with a gradient of star formation observed
for YSOs from north to south (Sung & Bessell, 2010; Venuti et al., 2018). NGC
2264 hosts the massive O7-type binary star S Monocerotis (S Mon), in the
north, and the famous Cone Nebula, a triangular projection of molecular gas
lying $\sim$8 pc south of S Mon. The YSO population of NGC 2264 is mainly
distributed in three sub-clusters: a cluster of pre-main sequence stars in the
north, in the vicinity of S Mon (Sung et al., 2009) and two clusters in the
center. The central sub-clusters are often labeled according to their dominant
objects, NGC 2264-IRS1, a B2-type star also known as Allen’s source and -IRS2,
a Class I protostar. IRS1 and IRS2 areas are also referred as NGC 2264-C and
-D, or the Spokes cluster for NGC 2264-IRS2 region (Teixeira et al., 2006).
These regions are still active sites of star formation, with many embedded
Class 0/I protostars observed in millimeter continuum and lines (Williams &
Garland, 2002; Peretto et al., 2006; Young et al., 2006; Cunningham et al.,
2016). Gas motions have also been reported from large scales, like the global
collapse of NGC 2264-C (Williams & Garland, 2002; Peretto et al., 2006), to
small scales like protostellar outflows (Maury et al., 2009; Cunningham et
al., 2016).

The paper is organized as follows. We introduce in Section 2 the _Herschel_
column density map and clump catalog of the NGC 2264 cloud. In Section 3, we
analyze the cloud structure using a multi-scale decomposition tool and divide
the NGC 2264 cloud into three subregions. We then compare in Section 4 the
physical properties of clumps in these subregions and discuss their spatial
distribution and mass segregation. In Section 5, we compare the YSO and clump
populations, in Section 6 we discuss mass segregation and propose a revised
history for star formation in the NGC 2264 cloud. Finally, we summarize our
conclusions in Section 7.

##  2 The NGC 2264 data set

###  2.1 _Herschel_ images, derived column density and temperature maps

Figure 1: Composite three-color _Herschel_ image of NGC 2264: PACS 70 $\mu$m
and 160 $\mu$m in blue and green, SPIRE 250 $\mu$m in red. The massive star S
Mon, the central protoclusters NGC2264 IRS1 and IRS2 and the Cone Nebula are
labeled. The blue component traces heated regions while earlier stage star-
forming sites, such as clumps and filaments, are traced by the red component.
The image has been rotated from the RA-Dec grid.  Figure 2:  The NGC 2264
cloud traced by its _Herschel_ column density. The clumps extracted by getsf
are indicated by ellipses, hatched for gravitationally bound clumps, empty
otherwise. A contour at $2\times 10^{22}$ cm-2 is drawn to highlight the
brightest parts. The resolution of the map, 18.2″, is shown in the lower-right
corner and a scale bar is given in the upper-left corner. The outline and
labels of the three subregions defined in Section 3.1 and used in the analysis
are shown in red. The location of the zoom of Fig. 3 is shown with a white
box.  Figure 3:  Zoom toward the central part of NGC 2264, imaged through its
_Herschel_ column density. Same caption as Fig. 2. The white and black crosses
show the location of the NGC2264 IRS1 and IRS2 sources, respectively. The
associated IRS1 and IRS2 protoclusters contain most of the gravitationally
bound clumps of this NGC 2264 subregion. The 15 most massive clumps
($M>9.3\,M_{\odot}$) are pinpointed with their number, the 16th is outside
this zoom.

NGC 2264 was observed by the _Herschel_ space observatory with the PACS
(Poglitsch et al., 2010) and SPIRE (Griffin et al., 2010) instruments as part
of the HOBYS (Motte et al., 2010) Key Program (OBSIDs: 1342205056 and
1342205057). Data were taken in five bands: 70 and 160 $\mu$m for PACS and
250, 350, and 500 $\mu$m for SPIRE with FWHM resolutions of 5.9$\arcsec$,
11.7$\arcsec$, 18.2$\arcsec$, 24.9$\arcsec$, and 36.3$\arcsec$, respectively.
Observations were performed in parallel mode, using both instruments
simultaneously, with a scanning speed of 20$\arcsec$s-1. This results in a
common PACS and SPIRE area of $1.0\degree\times 1.2\degree$, which corresponds
to 10 pc $\times$ 13 pc at a distance of 723 pc.

Data were reduced using the _Herschel_ Interactive Processing Environment
(HIPE, Ott, 2010) software, version 10.0.2751. SPIRE nominal and orthogonal
maps were separately processed and subsequently combined and reduced for de-
stripping, relative gains, and color correction with HIPE. PACS maps were
reduced with HIPE up to Level 1 and, from there up to their final version
(Level 3), using Scanamorphos v21.0 (Roussel, 2013). The composite three-color
(RGB = 250 $\mu$m/160 $\mu$m/70 $\mu$m) _Herschel_ image of the NGC 2264 cloud
is presented in Fig. 1.

High-resolution column density and temperature images, at the 18.2$\arcsec$
resolution of the 250 $\mu$m image, were built using the methods presented in
Palmeirim et al. (2013) and Men’shchikov (in prep.). In short, we fitted
pixel-by-pixel spectral energy distributions of the _Herschel_ images with
modified blackbody models to four, three, and two wavelengths (160/250/350/500
$\mu$m, 160/250/350 $\mu$m, and 160/250 $\mu$m) that were convolved to the
lowest resolution of the images available in the three sets of wavebands. The
higher-resolution information contained in the resulting density images,
obtained with the smaller number of wavelengths, was transferred
differentially to the 36$\arcsec$ resolution density image derived by fitting
four wavebands, thereby increasing its resolution by a factor of two. We
adopted a power law approximation to the dust opacity law per unit of mass at
submillimeter wavelengths, $\kappa_{\nu}=0.1\times(\lambda/\rm
300\leavevmode\nobreak\ \mu m)^{-\beta}$ cm${}^{2}\,$g-1, assuming a dust
emissivity index $\beta=2$ and a gas-to-dust ratio of 100. The resulting
column density and dust temperature images are presented in Figs. 2 and A1.
The column density map traces cloud structures from $\sim 2\times 10^{21}$
cm-2 to $2.7\times 10^{23}$ cm-2. The main cloud structure above
$\sim$4$\times 10^{21}$ cm-2 displays a ”Y” shape and consists of a hub of
three filaments connecting toward a NW-SE ridge. The highest column densities,
$N_{\rm H_{2}}=2-30\times 10^{22}$ cm-2, are reached at the center of NGC
2264, toward the two protostellar clusters NGC 2264-IRS1 and IRS2. Most of the
low to medium (column) density gas has a dust temperature of $\sim$14.5 K,
decreasing down to 11-12 K within some high-density filaments and increasing
up to 20-24 K toward IRS1 and IRS2. In the north, a large bubble of low
density, heated gas, with 18-27 K over $\sim$4 pc, is associated with a
rosette-shaped nebula observed in infrared and a couple of OB stars including
the Herbig Ae/Be star called W90 (Dahm, 2008).

Since Mon OB1 lies above the Galactic plane, the contamination of _Herschel_
column density and temperature images by other clouds along the same line of
sight is minimal (see Schneider et al. in prep. for a complete discussion of
contamination effects). Absolute uncertainties on $N_{\rm H_{2}}$, a factor of
2, and $T_{\rm dust}$, a few degrees, depend mostly on the assumption taken
for the dust mass opacity.

###  2.2 Clump catalog

Table 1: Sample catalog of clumps identified in NGC 2264 using the _Herschel_
column density map. The full table is shown in Table A4 and available in
electronic form through CDS.

# R.A. Dec. $A\times B$ PA Sig $N^{\rm peak}_{\rm H_{2}}$ $N^{\rm int}_{\rm
H_{2}}$ FWHMdecaafootnotemark: $a$ $T_{\rm dust}$ $M$ $\alpha_{\rm BE}$
$N^{\rm back}_{\rm H_{2}}$ [J2000] [J2000] [$\arcsec\times\arcsec$]
[$\degree$] [$\times 10^{21}\,\rm cm^{-2}$] [$\times 10^{21}\,\rm cm^{-2}$ ]
[pc] [K] [$M_{\odot}$] [$\times 10^{21}\,\rm cm^{-2}$] 1 100.5263 9.1742
$18.2\times 18.2$ 18.3 80.5 $18\pm 2$ $18\pm 2$ 0.03 $20\pm 2$ $1.8\pm 0.2$
0.3 $11.5\pm 0.2$ 2 100.2023 9.5775 $36.6\times 32.4$ 152.2 174.4 $80\pm 6$
$286\pm 10$ 0.1 $11.8\pm 0.3$ $30\pm 1$ 0.03 $17.4\pm 0.6$ 3 100.2944 9.9336
$27.4\times 26.2$ 24.5 104.8 $19\pm 2$ $41\pm 2$ 0.07 $15.7\pm 0.7$ $4.3\pm
0.2$ 0.21 $8.8\pm 0.2$ 4 99.8894 9.5995 $33.6\times 23.2$ 44.3 94.8 $30\pm 3$
$68\pm 4$ 0.07 $11.5\pm 0.4$ $7.0\pm 0.4$ 0.1 $13.0\pm 0.3$ 5 100.2081 9.0423
$30.1\times 23.4$ 168.7 76.2 $11.2\pm 0.8$ $21.0\pm 0.9$ 0.07 $12.3\pm 0.2$
$2.2\pm 0.1$ 0.31 $6.5\pm 0.1$ 6 100.2997 9.4868 $26.5\times 23.8$ 93.5 64.4
$209\pm 12$ $400\pm 15$ 0.06 $17.3\pm 0.7$ $41\pm 2$ 0.02 $61\pm 1$ 7 100.2741
9.5998 $31.3\times 26.3$ 7.2 56.5 $82\pm 11$ $195\pm 14$ 0.08 $15.0\pm 0.4$
$20\pm 1$ 0.05 $46\pm 3$ 8 100.272 9.5686 $22.8\times 22.8$ 22.6 47.7 $77\pm
11$ $90\pm 13$ 0.05 $15.4\pm 0.7$ $9\pm 1$ 0.07 $52\pm 2$ 9 100.3104 9.4536
$29.5\times 20.2$ 48.4 50.1 $44\pm 8$ $77\pm 9$ 0.06 $13.3\pm 0.4$ $8.0\pm
0.9$ 0.08 $22.6\pm 0.6$ 10 100.5549 9.0945 $31.8\times 22.5$ 115.5 42.3 $11\pm
2$ $22\pm 3$ 0.07 $12.6\pm 0.4$ $2.3\pm 0.3$ 0.31 $9.7\pm 0.5$ 11 99.9603
9.6866 $40.8\times 24.9$ 93.2 45.8 $13\pm 1$ $35\pm 1$ 0.09 $12.2\pm 0.2$
$3.6\pm 0.1$ 0.26 $6.4\pm 0.2$ 12 99.9953 9.779 $38.2\times 25.4$ 56.7 46.9
$5.9\pm 0.2$ $17.0\pm 0.3$ 0.09 $13.5\pm 0.3$ $1.7\pm 0.0$ 0.58 $2.9\pm 0.0$
13 100.2838 9.4998 $41.0\times 32.3$ 153.3 48.6 $138\pm 13$ $514\pm 21$ 0.11
$16\pm 2$ $53\pm 2$ 0.03 $41\pm 3$ 14 100.25 9.7985 $31.9\times 27.8$ 129.0
40.5 $5.9\pm 0.9$ $15\pm 1$ 0.08 $15.1\pm 0.3$ $1.6\pm 0.1$ 0.65 $7.1\pm 0.2$
15 100.224 8.9245 $38.6\times 28.0$ 174.2 44.6 $5.3\pm 0.4$ $16.0\pm 0.6$ 0.1
$13.0\pm 0.2$ $1.7\pm 0.1$ 0.61 $3.9\pm 0.1$ 16 100.2746 9.5619 $46.7\times
26.6$ 174.2 32.2 $67\pm 11$ $204\pm 15$ 0.11 $13.7\pm 0.7$ $21\pm 2$ 0.06
$45\pm 3$ 17 100.2961 9.5946 $37.4\times 25.9$ 166.2 31.2 $46\pm 13$ $100\pm
13$ 0.09 $14.0\pm 0.9$ $10\pm 1$ 0.1 $35\pm 3$ 18 100.1488 9.9097 $23.8\times
22.8$ 113.5 31.8 $5\pm 1$ $9\pm 2$ 0.05 $15.8\pm 0.4$ $0.9\pm 0.2$ 0.74
$5.2\pm 0.3$ 19 100.1259 9.8254 $37.1\times 24.9$ 155.3 39.4 $6\pm 1$ $16\pm
2$ 0.09 $16.5\pm 0.3$ $1.7\pm 0.2$ 0.69 $5.9\pm 0.2$ 20 100.3121 9.4895
$57.4\times 27.1$ 25.2 36.5 $105\pm 13$ $382\pm 19$ 0.12 $14.3\pm 0.4$ $40\pm
2$ 0.04 $55\pm 3$
aafootnotemark: $a$source deconvolved size at FWHM, $FWHM_{\rm dec}=(A\times
B-18.2^{2})^{1/2}$, minimized at half the beam size

Compact sources were extracted from the column density map at 18.2$\arcsec$
resolution (see Section 2.1) using getsf (v200124, Men’shchikov in prep.), a
new multi-scale, multi-wavelength source and filament extraction algorithm
based on the separation of structural components. The method is a major
improvement over its predecessors getsources, getfilaments, and getimages
(Men’shchikov et al., 2012; Men’shchikov, 2013, 2017). Original images are
spatially decomposed into a set of $99$ single-scale images, from two pixels
to a maximum scale of 4 times the maximum size of sources of interest,
5.6$\arcsec$ to 232$\arcsec$ (or 0.8 pc) for the NGC~2264 HOBYS field. The
decomposed images are processed and analyzed by getsf to separate three
structural components – sources, filaments, and background. The method
flattens the sources and filament components to equalize their noise and
background fluctuations, in order to reliably detect sources and filaments
using intensity (here, column density) thresholds.

Detection of sources and filaments is done in the single-scale flattened
images, whereas their measurements are done in the background-subtracted
original image. The resulting catalog contains, for each extracted source, its
peak and integrated column densities, $N_{\rm H_{2}}^{\rm peak}$ and $N_{\rm
H_{2}}^{\rm int}$, with uncertainties, its major and minor axis sizes at FWHM,
$A$ and $B$, and the position angle of its elliptical footprint (PA). Also
given is the source detection significance, $S$, which is a single-scale
analog of the signal-to-noise ratio and the column density of the subtracted
background, $N_{\rm H_{2}}^{\rm back}$. To measure the latter we averaged the
background map provided by _getsf_ over the source FWHM. A subset of this
catalog is given in Table 1 and the complete table can be found in Table A4.

We also list in Table 1 the source FWHM sizes deconvolved from the beam,
$FWHM_{\rm dec}$, which provide an estimation of the physical sizes and range
from 0.03 pc to 0.2 pc with a median value of 0.11 pc. The source average dust
temperatures, $T_{\rm dust}$, are listed with their associated uncertainties.
They are defined as the mean and dispersion, respectively, measured over the
source FWHM areas on the temperature map of Fig. A1. The measured temperature
are averaged along the line of sight of each clump and generally overestimate
by a few degrees the mean dust temperature of cold, shielded clumps (Hill et
al., 2012). $T_{\rm dust}$ ranges from 11.5 K to 22.7 K with a median of 13.9
K. The temperature distribution in Fig. A4 show little variations, as 85% of
the clumps have a temperature between 12 and 16 K.

A total of 256 sources has been extracted in NGC 2264 using getsf, they are
plotted over the column density map in Figs. 2 and 3. The mass of the clumps
is calculated from the integrated column density given by _getsf_ , $N_{\rm
H_{2}}^{\rm int}$ (see Table 1), following the equation:

$\displaystyle M$ $\displaystyle=$ $\displaystyle\mu_{\rm H_{2}}\,m_{\rm
H}\,d^{2}\,\Omega_{\rm b}\,N_{\rm H_{2}}^{\rm int}$ $\displaystyle=$
$\displaystyle 0.10\leavevmode\nobreak\ M_{\odot}\,\left(\frac{\Theta_{\rm
HPBW}}{18.2\arcsec}\right)^{2}\,\left(\frac{N_{\rm H_{2}}^{\rm
int}}{10^{21}\,\rm H_{2}\,cm^{-2}}\right),$

with $\mu_{\rm H_{2}}=2.8$ the molecular weight per hydrogen molecule, $m_{\rm
H}$ the hydrogen mass, $d=723$ pc the distance to NGC 2264 and $\Omega_{\rm
b}$ the beam solid angle. Masses range from 0.08 $M_{\odot}$ to 53 $M_{\odot}$
with a median of 0.9 $M_{\odot}$. The relative uncertainty of clump masses is
estimated to be less than 50% but the absolute uncertainty could be of a
factor of 2.

###  2.3 YSO catalog

YSOs are selected from the 2MASS/_Spitzer_ catalog established by Rapson et
al. (2014) on the eastern part of the Mon OB1 molecular complex. The _Spitzer_
map used by Rapson et al. (2014) covers most of the _Herschel_ field, but
small areas in the south, east and west, within which 10 clumps have been
detected. Other YSO catalogs built over smaller areas (like one of, e.g., Kuhn
et al., 2014) are not used here to keep the homogeneity of the YSO catalog.
Rapson et al. (2014) use _Spitzer_ IRAC (3.6-8$\micro$m) and MIPS 24$\micro$m
data to select YSOs from their IR excess. They are thus sensitive to class 0/I
sources while Cody et al. (2014) are not (since they use only IRAC data).
Moreover, as opposed to Venuti et al. (2018) that present a spectroscopic
study based on Gaia-ESO-Survey data, their spatial coverage is complete which
is important for our study.

Among the 6381 YSOs within the field of view of the _Herschel_ column density
map, there are 87 Class 0/I protostars and 398 Class II pre-main sequence
stars. Class III sources have been excluded from the studied sample because
they are evolved YSOs, less relevant for comparison with the gas distribution.
Besides, the Class III population in the catalog of Rapson et al. (2014) is
significantly contaminated with field stars. The classification of YSOs was
carried out by Rapson et al. (2014) using a color-based method developed by
Gutermuth et al. (2009). Our sample also does not include the $\sim$20
intermediate- and high-mass sources that started forming a few $10^{6}$ years
ago, i.e. at the same time as the Class IIs of Rapson et al. (2014).

##  3 Analysis of the cloud structure in NGC 2264

Based on the YSO spatial distribution, the NGC 2264 star-forming region
separates into two main subregions (Sung et al., 2008, 2009): the S Mon area
and the region which encloses the two clusters associated with IRS1 and IRS2
(see Section 1). In line with these studies, the three-color, temperature and
column-density _Herschel_ images of Figs. 1-2 and A1 argue to separate the
intermediate-column density, hotter northern subregion from the high-column
density central subregion hosting the IRS1 and IRS2 protoclusters. The
remaining part of the NGC 2264 cloud, which has intermediate column density
and colder temperatures, is then labeled the southern subregion.

###  3.1 Splitting the NGC 2264 cloud into 3 subregions

Table 2: Global characteristics and cloud structure of NGC 2264 and its three
subregions and fit values for the total wavelet power spectrum of NGC 2264 and
for the Gaussian and coherent power spectra of the northern, southern and
central regions.

Region | Area |  $\overline{N_{\rm H2}}$\,a\,afootnotemark: $\,a$ |  $N_{\rm H2}^{\rm back}$\,b\,bfootnotemark: $\,b$ |  $\overline{T_{\rm dust}}$\,a\,afootnotemark: $\,a$ | MnGSeg | $\gamma$ | $A$ | $P_{0}$ | Noise
---|---|---|---|---|---|---|---|---|---
name |  [°2], [pc2] |  [$\times 10^{21}$ cm-2] | [K] | components | powerlaw | $[$ |  $\times 10^{42}\leavevmode\nobreak\ $(H2 cm-2)2 | $]$
NGC 2264 |  1.16, $\sim$185 | 3.9 | 4.7 | 14.7 | all | $-2.3\pm 0.1$ | $35\pm 7$ | $-$ | $(5.0\pm 0.5)\times 10^{-3}$
Northern |  0.38, $\sim$61 | 2.9 | 4.0 | 15.4 | Gaussian | $-4.8\pm 0.1$ | $0.06\pm 0.01$ | $0.10\pm 0.02$ | $(3.3\pm 0.5)\times 10^{-3}$
|  |  |  |  | Coherent | $-2.31\pm 0.09$ | $9.0\pm 0.5$ | $-$ | $(3.3\pm 0.5)\times 10^{-3}$
Central |  0.26, $\sim$41 | 6.6 | 7.3 | 14.9 | Gaussian | $-3.6\pm 0.1$ | $0.08\pm 0.02$ | $0.09\pm 0.02$ | $(3.3\pm 0.5)\times 10^{-3}$
|  |  |  |  | Coherent | $-2.3\pm 0.1$ | $299.0\pm 0.6$ | $-$ | $(3.3\pm 0.5)\times 10^{-3}$
Southern |  0.52, $\sim$83 | 3.3 | 4.3 | 14.1 | Gaussian | $-4.02\pm 0.07$ | $0.09\pm 0.01$ | $0.13\pm 0.01$ | $(3.3\pm 0.5)\times 10^{-3}$
|  |  |  |  | Coherent | $-2.43\pm 0.09$ | $3.4\pm 0.5$ | $-$ | $(3.3\pm 0.5)\times 10^{-3}$

aafootnotemark: $a$

Typical column density and temperature, calculated as the average of the map
in this area
bbfootnotemark: $b$Typical background column density, calculated as the median
background of clumps located in this area

In order to refine the outlines of the three NGC 2264 subregions, we performed
a multiscale analysis of the NGC 2264 cloud. We used complex wavelet
transforms described by Robitaille et al. (2014, 2019) to first calculate the
power spectrum of the complete NGC 2264 region. While corresponding very well
to the classic Fourier power spectrum, this method goes further by making it
possible to visualize the spatial distribution of density fluctuations for any
scale of the power spectrum. The decomposition is done with the directional
and complex Morlet wavelet defined in the Fourier space as

$\begin{split}\hat{\psi}(\@vec{k})&=\textrm{e}^{-|\@vec{k}-\@vec{k}_{0}|^{2}/2}\\\
&=\textrm{e}^{-[(u-|\@vec{k}_{0}|\cos\theta)^{2}+(v-|\@vec{k}_{0}|\sin\theta)^{2}]/2},\end{split}$
(2)

where $\@vec{k}=[u,v]$ is the wavenumber, $\theta$ is the wavelet azimuthal
direction and the constant $|\@vec{k}_{0}|$ is set to $\pi\sqrt{2/\ln
2}\approx 5.336$ to ensure that the admissibility condition is almost met
(Kirby, 2005). From this decomposition, the amount of power for the density
fluctuations as a function of spatial scales is calculated following the
relation

$P^{W}(\ell)=\left\langle\left\langle|\tilde{f}(\ell,\@vec{x},\theta)|^{2}\right\rangle_{\theta}\right\rangle_{\@vec{x}},$
(3)

where $\langle\rangle$ represents the averaging operation. The spatial scale
$\ell$ is then converted to the Fourier wavenumber $k$ following the relation
$k=|\@vec{k}_{0}|/\ell$.

Figure 4 shows the wavelet power spectrum of NGC 2264, corrected for the noise
level and the $18.2\arcsec$ beam. It can be fitted by a power law relation
$P(k)=A\,k^{\gamma}$ (see fit parameters in Table 2), except for excess of
power located at $\sim 0.15$ arcmin-1 and $\sim 1.5$ arcmin-1corresponding
respectively to scales of $\sim 1.4$ pc and $\sim 0.1$ pc. Figure 5 shows the
spatial distribution of the power density fluctuations averaged over $\theta$,
$|\tilde{f}(\ell,\@vec{x})|^{2}$, for these two scales as well as the $0.47$
arcmin-1 (or 0.4 pc) scale located between the two power excess. The first
excess at 1.4 pc corresponds to the large-scale mass reservoir associated with
the central part of the NGC 2264 cloud. It dominates in terms of density
fluctuation at this spatial scale (see Fig. 5, Left panel) like do massive
hubs or ridges in, e.g., the Cygnus X, Vela C, and NGC 6334 cloud complexes
(Schneider et al., 2010; Hill et al., 2011; Tigé et al., 2017). This excess is
also measured on NGC 2264 using the $\Delta$-variance method (Schneider et al.
in prep.). The 0.1 pc-scale image (see Fig. 5, Right panel) displays smaller
scale structures, associated with clumps.

The intermediate scale of 0.4 pc, which separates the scales associated with
the mass reservoir of the central ridge and the mass reservoir of small-scale
clumps, was chosen to refine the division of the NGC 2264 cloud into three
subregions. The outline of the northern, central, and southern subregions are
set to go through saddle points (see Fig. 5, middle panel). The global
characteristics of these subregions observed on the _Herschel_ column density
and temperature images are listed in Table 2.

Figure 4: The Fourier (solid lines) and wavelet (symbols) power spectra of the
NGC 2264 region. Before fitting a $P(k)=A\,k^{\gamma}$ relation (dashed
curve), the corrected power spectra are subtracted by the noise level (plateau
at the end of the original power spectrum, green dotted horizontal line) and
divided by the empirical SPIRE 250 $\mu$m beam. The power spectra present two
bumps, located at 1.4 pc and 0.1 pc (vertical blue dot-dashed lines). The
intermediate scale chosen to separate the three NGC 2264 subregions is
indicated by a red dot-dashed vertical line.  Figure 5: Power density
fluctuations of NGC 2264 measured by MnGSeg and averaged over $\theta$ for
three scales: 0.15 arcmin-1 (or 1.4 pc, left panel), 0.47 arcmin-1 (or 0.4 pc,
middle panel) and 1.5 arcmin-1 (or 0.1 pc, right panel). Images have been
rotated from the RA-Dec grid (gray lines) to facilitate the wavelet
decomposition. The yellow lines in the middle panel separate the three
subregions of NGC 2264.

###  3.2 Segmentation of the coherent and Gaussian components of the cloud
structure

We applied the Multiscale non-Gaussian Segmentation (MnGSeg) technique in
order to investigate the hierarchical properties of the cloud structure in the
three subregions of NGC 2264. This technique allows to separate the random
spatial density fluctuations of a cloud from the dense, coherent structures,
usually associated with star formation activities. While we expect the
Gaussian component of a cloud to only consist of evanescent structures of the
interstellar medium, its coherent component contains cores, clumps, hubs,
ridges and other filamentary structures which correspond to the gas mass
reservoir of present star formation. The non-Gaussian segmentation is done on
wavelet coefficients as a function of orientations $\theta$ and scales $\ell$
(see Robitaille et al. 2019 for more details on the technique). The amount of
power for the density fluctuations as a function of spatial scales,
$|\tilde{f}(\ell,\@vec{x})|^{2}$, can be calculated according to the spatial
coverage of areas of interest following the relation

$P^{W}(l)=\frac{1}{N_{\mathbb{L}}}\sum_{\@vec{x}}\left\langle\mathbb{L}(|\tilde{f}(\ell,\@vec{x},\theta)|)|\tilde{f}(\ell,\@vec{x},\theta)|^{2}\right\rangle_{\theta},$
(4)

where

$\mathbb{L}(|\tilde{f}_{l,\theta}(\@vec{x})|)=\left\\{\begin{array}[]{ll}1&\textrm{if
$\@vec{x}$ is inside the area of interest}\\\ 0&\textrm{if $\@vec{x}$ is
outside the area of interest},\\\ \end{array}\right.$ (5)

and

$N_{\mathbb{L}}=\sum_{\@vec{x}}\mathbb{L}(|\tilde{f}_{l,\theta}(\@vec{x})|).$
(6)

Figure 6 presents the Gaussian (random) and coherent wavelet power spectra for
the three subregions of NGC 2264 and Table 2 lists the parameters of their
fitted relation $P(k)=A\,k^{\gamma}+P_{0}$ for the Gaussian component and
$P(k)=A\,k^{\gamma}$ for the coherent part. For all subregions, the coherent
component dominates in terms of power over the Gaussian components. Moreover,
the central subregion presents a Gaussian component which resembles, in terms
of both power level and power law index, those fitted for the northern and
southern subregions. In contrast, the coherent component of the central
subregion possesses a power level several hundred times higher than that of
the northern and southern subregions. The coherent spectrum of the central
subregion also displays more irregularities compared to the coherent spectra
of the northern and southern parts (see Fig. 6b). We notably recover the two
power excess observed in Fig. 4. As shown by Robitaille et al. (2019), the
small-scale spectra flattening modelled by the variable $P_{0}$ for the
Gaussian components only is associated to the cosmic infrared background
signal.

Figure 6: The random Gaussian (in a) and coherent (in b) wavelet power spectra
calculated by MnGseg for the three NGC 2264 subregions. The black and red
dotted curve represents the fitted power law relations to the northern and
central subregions. The coherent power spectrum of the central subregion
stands out with $\sim$100 times more power and bumps located at 1.4 pc and 0.1
pc (vertical blue dashed lines).

Recently Robitaille et al. (2020) discussed and proved, using statistical
models, that the non-Gaussian segmentation performed by MnGSeg can also be
interpreted as the separation of a component of multifractal nature, the
coherent part, from a monofractal component, the random Gaussian part. The
multifractal nature of the coherent component means that its hierarchical
structures are defined by a collection of power law indexes rather than by a
single one, as it is the case for the monofractal geometry of the Gaussian
component. These different fractal properties explain why the power spectra of
the coherent components in Fig. 6b are more diverse and complex than those of
the Gaussian components shown in Fig. 6a. It also suggests that the coherent
component of a cloud (see Fig. 6b and Table 2) is the one that could reveal
the complexity level of cloud structures, which increases with the creation of
large gravity potentials such as hubs and ridges. A more detailed analysis of
the multifractal properties of the NGC 2264 cloud, its subregions, and clumps’
local environment will be done by Robitaille et al. (in prep.).

##  4 Analysis of the population of clumps in NGC 2264

After estimating the completeness level of the catalogs of clumps identified
in the three subregions of NGC 2264 (see Section 4.1), we characterize the
main physical properties of clumps (see Section 4.2) and evaluate their
boundedness (see Section 4.3). We then investigate the spatial distribution of
clumps (see Section 4.4) and their mass segregation (see Section 4.5). Table 3
lists the main statistical parameters of the NGC 2264 cloud and its three
subregions.

###  4.1 Completeness of the clump catalogs

To accurately compare the clump’s properties between subregions, we estimated
the completeness level and how it varies across the map. For this purpose, we
injected a synthetic population of 1170 sources over the background image of
NGC 2264, which is produced by getsf during the clump extraction process (see
Section 2.2). Synthetic sources were split into 9 bins logarithmically spaced
between 0.25 $M_{\odot}$ and 4.0 $M_{\odot}$, with a constant number of 130
sources per bin. Their chosen density profile is that of Bonnor-Ebert spheres
with a central density increasing with the clump mass, in agreement with the
profile observed for clumps. These synthetic clumps, once convolved by our
18.2$\arcsec$ angular resolution, display FWHM sizes of about 0.12 pc (or
35$\arcsec$) corresponding to the median size of extracted sources. We ran the
extraction algorithm getsf on the synthetic image with the same parameters as
for the observations. In total, 695 out of 1170 (59%) sources have been
recovered.

Figures A2a-b show the detection rate of synthetic sources injected on the NGC
2264 background image. From the detection rate versus mass curve of Fig. A2a,
we estimated a global 90% completeness level of $\sim$1.7 $M_{\odot}$. 74
(29%) clumps of Table 1 lie above this completeness level.

The completeness level of any source extraction procedure does however depend
on the intensity of the source background. We therefore computed, for each
identified sources, its contrast, defined as $C=N_{\rm H_{2}}^{\rm
peak}/N_{\rm H_{2}}^{\rm back}$ and plot the detection rate against source
contrast in Fig. A2b. A minimum contrast of 0.4 is required to detect a
source, while all sources with $C>1.7$ are detected. The 90% completeness
level is reached for a source contrast of $C=1.1$, that is for clumps with
$N_{\rm H_{2}}^{\rm peak}=1.1\times N_{\rm H_{2}}^{\rm back}$. As shown in
Fig. 2 and Tables 1 and 2, the background level varies strongly from the
northern or southern subregions and central part of NGC 2264. We therefore
used the median value of the clumps’ background to characterize each
subregion: $\overline{N_{\rm H_{2}}^{\rm back}}=4\times 10^{21}$, $4.3\times
10^{21}$ and $7.3\times 10^{21}$ cm-2 in the northern, southern and central
subregions, respectively. Using the relation of mass versus peak column
density found for both synthetic and observed clumps ($M\propto(N_{\rm
H2}^{\rm peak})^{0.95}$, see Fig. A3a), we computed the associated mass
threshold for clumps. We obtained a 90% completeness level of $\sim$1.5
$M_{\odot}$ for both the northern and southern subregions and a completeness
level about two times larger, 2.7 $M_{\odot}$, for the central subregion of
NGC 2264. As a consequence, some low mass clumps in the center may be
overlooked by the extraction algorithm. The number of clumps with masses above
these 90% completeness levels are 19, 22, and 33 in the northern, southern,
and central subregions, respectively.

###  4.2 Main physical properties of clumps

Table 3:  Distribution of clumps and YSOs in NGC 2264 and its subregions.

Region | Clumps | YSOs
---|---|---
name | Totala,ba,bfootnotemark: $a,b$ | Boundb,cb,cfootnotemark: $b,c$ | Unboundb,cb,cfootnotemark: $b,c$ | $\overline{\Sigma_{\rm clump}}$ |  $Q$ddfootnotemark: $d$ |  $\Lambda_{\rm MSR}^{\rm med}$ddfootnotemark: $d$ | Totaleefootnotemark: $e$ | Class 0/I | Class II
NGC 2264 | 256/100% | 92/36% | 164 |  $\sim$1.4 pc-2 | $\simeq 0.7$ | 7.8 | 485/100% | 87 | 398
Northern | 74/29% | 21/28% | 53 |  $\sim$1.2 pc-2 | $\simeq 0.8$ |  $\simeq$1 | 156/32% | 10 | 146
Central | 97(96)/38% | 48/50% | 49(48) |  $\sim$2.4 pc-2 | $\simeq 0.8$ | 3.7 | 302/62% | 69 | 233
Southern | 85(76)/33% | 23(22)/27% | 62(54) |  $\sim$0.9 pc-2 | $\simeq 0.6$ |  $\simeq$1 | 27/6% | 8 | 19

aafootnotemark: $a$

Number of clumps in each subregions and their corresponding fraction of the
total population.
bbfootnotemark: $b$The number of clumps in the area where YSOs have been
detected is given in parentheses.
ccfootnotemark: $c$Gravitationally bound clumps have a $\alpha_{\rm BE}<2$
parameter in Table 1. The fraction of bound clumps in the subregion population
is indicated.
ddfootnotemark: $d$The clustering $Q$ parameter is calculated from Eq. 8. The
median mass segregation ratio, $\Lambda_{\rm MSR}^{\rm med}$, measured for the
$N_{\rm MST}$ = 4 to 15 most massive clumps, are estimated from Fig. 11.
eefootnotemark: $e$Total number of Class 0/I and Class II YSOs in each
subregions and their corresponding fraction of the total population.

Figure 7: Distribution of clump size (in a) and clump mass (in b) for the
three subregions of NGC 2264: central (black hatched histograms), northern
(red histograms), and southern (blue histograms) subregions. The central
subregion has an excess of high-mass clumps compared the northern and southern
subregions. The angular resolution of our observations, 18.2$\arcsec$, is
given in a. The completeness levels, $\sim$2.7$M_{\odot}$ in the central
subregion and $\sim$1.5$M_{\odot}$ in the northern and southern subregions,
are indicated in b with dotted lines.

The size and mass distributions of clumps in the northern, central and
southern subregions are presented in Figs. 7a-b. As for the clumps sizes no
significant variations have been observed between subregions (see Fig. 7a).
The size distributions are all centered around a median value of $\sim$35″,
corresponding to a deconvolved size of FWHM${}_{\rm dec}\sim$ 0.1 pc at 723
pc. The three subregions also host a couple of clumps with FWHM sizes close to
the beam, 18.2$\arcsec$, and 9 clumps with sizes larger than 57$\arcsec$ (or
0.2 pc). At a scale of $\sim$ pc, clumps will likely fragment into smaller and
denser cores, which could be the main mass reservoirs of individual
protostars. Such cores have been observed in the two central protoclusters,
IRS1 and IRS2 by Peretto et al. (2006) and Cunningham et al. (2016).

As for the mass distribution of clumps, it strongly varies between subregions
(see Fig. 7b). While they are similar in the northern and southern subregions,
the mass distribution in the central subregion is strongly shifted toward
higher masses. Out of the 29 clumps with masses above 4 $M_{\odot}$, 24 are
located in the central subregion.

###  4.3 Gravitational stability of clumps

Figure 8: Mass versus size diagram (in a) and mass distribution (in b) for the
256 clumps detected in the NGC 2264 cloud. Gravitationally bound and unbound
clumps, with respectively $\alpha_{\rm BE}<2$ and $\alpha_{\rm BE}\geq 2$, are
located with red (resp. blue) markers (in a) and sum up in a red (resp. blue)
histogram (in b). a) The critical Bonnor-Ebert sphere model ($\alpha_{\rm
BE}=2$) at the median clump temperature, $T_{\rm dust}=13.9$ K, is plotted as
a black solid line. The physical size of the beam at a distance of 723 pc is
plotted as a vertical doted line. b) The global 90% completeness level is
indicated with a doted line.

In _Herschel_ studies, the self-gravitating status of clumps is usually
assessed using the Bonnor-Ebert ratio, $\alpha_{\rm BE}=R_{\rm dec}/R_{\rm
BE}$, where $R_{\rm dec}$ is the deconvolved clump radius (here $FWHM_{\rm
dec}$ and $R_{\rm BE}$ the radius of a critical Bonnor-Ebert sphere of the
same mass, $M$, and temperature, $T_{\rm dust}$ as the source,

$R_{\rm BE}=\frac{M\,\mu_{\rm H_{2}}\,m_{\rm H}\,G}{2.4\times k\,T_{\rm
dust}},$ (7)

where $G$ and $k$ are the gravitational and Boltzman constants. This parameter
estimates the ratio between the thermal support and gravitational force and
clumps with $\alpha_{\rm BE}<2$ are considered as gravitationally bound. The
Bonnor-Ebert ratio should however be considered as a rough estimator of the
gravitational boundedness of the clump, since it does not include the effects
of turbulence, magnetic fields, or external pressure.

Although $\alpha_{\rm BE}$ parameters theoretically depends on the mass, size,
and temperature of the clump, the latter, which varies little within the
sample ($13.9^{+3}_{-1}$ K, see Fig. A4), has a small influence here. As
illustrated by the mass versus size diagram shown in Fig. 8a, the separation
between bound and unbound clumps appears close to horizontal in this
logarithmic representation. This implies that a threshold on the Bonnor-Ebert
parameter approximately corresponds to a threshold in mass, $\alpha_{\rm
BE}<2\Longleftrightarrow M\gtrsim$ 1-2 $M_{\odot}$. This appears more clearly
on the mass distributions shown in Fig. 8b. Bound clumps are significantly
more massive than unbound clumps, with a median mass of 2.6 $M_{\odot}$ vs 0.5
$M_{\odot}$, respectively.

In the whole NGC 2264 cloud and following the Bonnor-Ebert criterium, 36% of
the clumps are gravitationally bound. In more detail, half the clumps of the
central subregion are bound while they are only $\sim$30% in the northern and
southern subregions (see Table 3). This result is in line with the variation
of column-density background in the central subregion when compared with the
southern and northern subregions (see Table 2).

###  4.4 Spatial distribution of clumps

Figure 9: Distribution of the distance to the first nearest neighbor for
clumps in the central (black hatched histogram), northern (red histogram), and
southern (blue histogram) subregions of NGC 2264. Clumps are 1.6 times more
closely packed in the central subregion than in the two others.

Despite the different completeness limits of the three NGC 2264 subregions
(see Section 4.1), clumps appear homogeneously distributed: 74, 97, and 85 in
the northern, central, and southern subregions, respectively (see Table 3), of
which $\sim$30% lie above their 90% completeness limit. Note, however, that
the density of clumps $\overline{\Sigma_{\rm clump}}$, defined as the ratio
between the number of clumps and the subregion area, is about twice in the
central subregion that in the northern and southern subregions (see Table 3).

To further investigate the spatial distribution of clumps, we first applied
nearest neighbor statistics, using the distance from the center of each clump
to the peak position of its nearest neighbor. The resulting distributions of
nearest-neighbor clump separation are plotted in Figure 9 for the three
subregions of NGC 2264. In the northern and southern subregions, clumps have
similar median distances, 0.34 pc and 0.36 pc respectively, and cannot be
statistically distinguished, according to a Kolmogorov-Smirnov (KS) test. The
nearest neighbor distribution of the central subregion stands out with a large
excess of clumps at short distance, $\sim$0.1–0.2 pc, and a median clump
separation of $\sim$0.22 pc, which is 1.6 times smaller than in the northern
and southern subregions. A KS test rejects, with a p-value below $10^{-4}$,
the hypothesis that the central and northern $+$ southern subregions might
have a common distance distribution. Interestingly, the clumps at the center
of NGC 2264 are closely packed, with separations of about twice their median
FWHM size, $\sim$0.11 pc (see Section 4.2), and thus once their outer
diameter. They are mostly located in the two IRS1 and IRS2 protoclusters of
NGC 2264 (see Fig. 3).

We also computed the $Q$ parameter (Cartwright & Whitworth, 2004) for the NGC
2264 cloud and its three subregions (see Table 3). The $Q$ parameter methods
is based on the minimum spanning tree (MST), which is the set of straight
lines (the edges) connecting a given set of points (here the clumps’ center)
without closed loops, such that the sum of all edges lengths is minimal. $Q$
is defined as the ratio between the normalized mean edge length calculated by
the MST, $\overline{l_{\rm edge}}$, and the mean clump separation,
$\overline{s}$:

$Q=\frac{\overline{l_{\rm edge}}}{\overline{s}}.$ (8)

$Q$ values above 0.8 are associated with centrally concentrated spatial
distribution, while lower $Q$ values indicate sub-clustering. With $Q\simeq
0.7$, the NGC 2264 cloud overall displays only a moderate amount of sub-
clustering for its clump population. Locally, the southern subregion appears
to be more subclustered compared to the central and northern subregions
($Q\simeq 0.6$ versus $Q\simeq$ 0.8). On the column density map (see Fig. 2),
we indeed observed that its clumps are distributed either in a North-South
filament joining the central subregion or in the south-western part of NGC
2264 .

###  4.5 Mass segregation of clumps

Figure 10: Local surface density $\Sigma_{6}$ as a function of the mass of the
clump for the central subregion of NGC 2264 (in a, black markers) and in the
northern and southern subregions (in b, red and blue markers, respectively).
The potential correlation between $\Sigma_{6}$ and $M$ in a,
$\Sigma_{6}\propto M^{0.47}$, is represented by a dashed blue line, in both a
and b. Completeness levels are indicated by vertical dotted lines.

Figure 11: Mass segregation ratio, $\Lambda_{\rm MSR}$, as a function of the
numbers of clumps, $N_{\rm MST}$, in the NGC 2264 cloud (in a) and in its
three subregions (in b). Error bars represent the $\pm 2\sigma$ uncertainties.
Values above $\Lambda_{\rm MSR}\simeq 1$ (black solid line) suggest mass
segregation. Completeness levels are indicated with vertical dashed lines. a)
The median segregation ratio for the 15 most massive clumps of NGC 2264,
calculated over $N_{\rm MST}=4$ to 15, is $\Lambda_{\rm MSR}^{\rm med}=7.8$
(red segment). b) The $\Lambda_{\rm MSR}$ function of clumps in the central
subregion (black symbols) is three to two times larger than the one in the
northern and southern subregions (in red and blue, respectively), over the
range of $N_{\rm MST}=4-33$.

Mass segregation refers to a difference between the spatial distribution of
massive objects compared to that of their lower-mass counterparts. The methods
used to quantify mass segregation have evolved with our current view on the
subject and are associated to slightly different definitions, but in general,
they all compare the distribution of high- and low-mass stars. Amongst them,
the local surface density $\Sigma_{\rm j}$ (Maschberger & Clarke, 2011), and
mass segregation ratio $\Lambda_{\rm MSR}$ (Allison et al., 2009), do not
require the specific definition of a cluster center and are the most widely
used. They most probably also give the most robust results when combined
(e.g., Parker & Goodwin, 2015). We applied both methods to measure the mass
segregation of clumps in NGC 2264 and its subregions, using again the clumps
peak position as a reference. The impact of the clumps spatial extent in these
statistics will be investigated in future studies (Thomasson et al. in prep.).

The first method calculates the local surface density of source, $\Sigma_{\rm
j}$, within an area encompassing the $j$th nearest neighbor, at a distance of
$r_{\rm j}$:

$\Sigma_{\rm j}=\frac{j-1}{\pi r_{\rm j}^{2}}.$ (9)

As proposed by Maschberger & Clarke (2011), we took $j=6$, as it constitutes a
good compromise between estimating the local density and reducing low-numbers
fluctuations (see also Casertano & Hut, 1985).

Figures 10a-b present the $\Sigma_{6}$ versus $M$ diagrams that investigate if
high-mass clumps are in denser groups than their lower-mass counterparts.
Since the clump populations of the northern and southern subregions exhibited
similar behaviors in Sections 4.2–4.4., we assembled their clump samples. In
contrast, we kept the central subregion alone since its clump sample had
rather different characteristics. In the central subregion, the local surface
density correlates with the mass of the clumps, with a Pearson correlation
coefficient of 0.75 (see Fig. 10a). However, when accounting for the 90%
completeness level of 2.7 $M_{\odot}$ (see Section 4.1), one can not exclude
low-mass clumps in high-density groups have not been detected. Therefore, the
most robust result of this $\Sigma_{6}$ analysis is that high-mass clumps in
the central subregions are only found in high-density groups. The 15 most
massive, $>$9.3 $M_{\odot}$, clumps in NGC 2264, closely packed in the IRS1
and IRS2 protoclusters (see Fig. 3 and Section 4.4), have a median
$\Sigma_{6}$ density about three times that of all clumps in NGC 2264 center.
Conversely, the lower-mass clumps in the northern and southern subregions do
not present a privileged location in high- or low-density groups according to
their mass (see Fig. 10b).

The second method to quantify mass segregation compares the MST of the most
massive objects (here clumps) of a sample with the MST of randoms subsets of
objects in this sample (see Allison et al., 2009). The mass segregation ratio,
$\Lambda_{\rm MSR}$, is calculated as

$\Lambda_{\rm MSR}(N_{\rm MST})=\frac{\overline{l_{\rm random}}}{l_{\rm
massive}}+\frac{\sigma_{\rm random}}{l_{\rm massive}},$ (10)

where $l_{\rm massive}$ is the length of MST for the $N_{\rm MST}$ most
massive clumps and $\overline{l_{\rm random}}$ is the average length of MST
for $N_{\rm MST}$ random clumps. It was computed for 500 sets of $N_{\rm MST}$
random clumps along with the associated standard deviation, $\sigma_{\rm
random}$. A value of $\Lambda_{\rm MSR}\approx 1$ indicates that the spatial
distribution of massive clumps is comparable to that of other clumps and
therefore that there is no mass segregation. Larger $\Lambda_{\rm MSR}$ values
suggest that massive clumps are more concentrated than their lower-mass
counterparts.

Figures 11a-b display $\Lambda_{\rm MSR}$, for an increasing number, $N_{\rm
MST}$, of the most massive clumps for the NGC 2264 cloud and its subregions.
The clump population of NGC2264 appears to be strongly mass segregated, with
values of $\Lambda_{\rm MSR}$ around 7.8 for $N_{\rm MST}\leq 15$. If we
consider only the central subregion, which contains the most massive clumps,
segregation is still present, but the $\Lambda_{\rm MSR}$ values decreases to
$\sim$3.7. This decrease is explained by smaller $l_{\rm random}$ values for
clumps distributed in the central subregion than over the whole NGC 2264
cloud. In Fig. 11a, $\Lambda_{\rm MSR}$ drops to 4 for $N_{\rm MST}=16$, due
to the fact that the 16th most massive clump is the first one located outside
the IRS1 and IRS2 protoclusters. Mass segregation remain significant, with
$\Lambda_{\rm MSR}$ values above 1 with more than 2 sigma uncertainty, up to
$N_{\rm MST}\simeq 30$. In contrast, for the clump samples of the northern and
southern subregions, Fig. 11b do not reveal any mass segregation. The
$\Lambda_{\rm MSR}$ plateau observed are listed in Table 3.

##  5 Combined analysis of the YSO and clump populations in NGC 2264

In this section, we analyze the YSO content of the NGC 2264 subregions
(Section 5.1) and quantify the link between the YSO and clump populations
(Section 5.2).

###  5.1 Distribution in the NGC 2264 subregions

Figure 12: Spatial distribution of the clump (black ellipses) and YSO
populations in the NGC 2264 cloud, as taken from the catalogs of Table 1 and
Rapson et al. (2014). Bound clumps are represented with filled ellipses. Class
0/I (blue star markers) and Class II (red star markers) sources were
identified in the area outlined by black dotted lines. The three subregions of
NGC 2264 (see Section 3.1) are outlined and labeled in black. Clumps are much
more homogeneously distributed over the NGC 2264 cloud than YSOs.

The spatial distribution of the clump and YSO populations is illustrated in
Fig. 12. The clump population is rather homogeneously distributed over the
whole _Herschel_ area (see Section 4.4) even if it obviously follows the gas
concentration toward the two IRS1 and IRS2 protoclusters and the ”Y”-shaped
filament. The YSOs themselves cluster in three places: strongly around the
IRS1 and IRS2 protoclusters and more distributed around the S Mon massive
star.

Table 3 lists the number of YSOs regardless of their class, the number of
Class 0/I protostars and Class II pre-main sequence stars found by Rapson et
al. (2014) in NGC 2264. We distributed this YSO population among the three
subregions of the NGC 2264 cloud defined in Section 3.1. Unlike the spatial
distribution of clumps in subregions, a very uneven distribution is observed
for the YSOs. The central subregion, which accounts for 22% of the whole NGC
2264 cloud area and contains 38% of the detected clumps (see Tables 2 and 3)
indeed gathers up to 62% (302 out of 485) of the YSO population. In contrast,
the southern subregion, which covers 45% of the whole cloud, contains 33% of
the clumps but only 6% (27 out of 485) of the YSOs. This clear lack of YSOs in
this subregion cannot be explained by the small area of the _Herschel_ image
not investigated for YSOs by Rapson et al. (2014) (see Fig. 12). The northen
subregion show a more balanced distribution with 33%, 29%, and 32% of the
cloud area, clump and YSO populations.

Regarding the evolutionary class of YSOs, the northern subregion is almost
exclusively populated by Class II sources (146 out of 156, 95%, see Table 3).
On the other hand, the distribution is more balanced in the central and
southern subregions: 23%-30% of the YSOs are Class 0/I protostars and the
complementary 70-77% are Class II sources. Finally, we found that the 69 Class
0/I in the central subregion represent a large majority (79%, see Table 3) of
this younger YSO population in the whole NGC 2264 cloud.

###  5.2 Link between the clump and YSO populations

Figure 13: Distribution of the distance to the first nearest neighbor, $nnd$,
from YSOs to clumps (in a and c) and from clumps to YSOs (in b and d). YSOs
are Class 0/I in the upper panel (a-b), Class II in the lower panel (c-d).
Distributions are presented for the central subregion (black hatched
histograms) and for the northern and southern subregions, combined in the
upper panel (purple histogram) and separated in the lower panel (red and blue
histograms, respectively). Distributions have been normalised to facilitate
the comparison involving small populations of YSOs (Class 0/I from northern
and southern subregions in a, Class II from southern subregions in c). The red
dashed vertical line indicates the median outer radius of clumps, calculated
as twice the median FWHM: $R_{\rm out}^{\rm med}=2\,{FWHM}^{\rm med}=0.25$ pc.

We investigated the spatial correlation between the clump and YSO populations
found in the different subregions of NGC 2264 using the nearest neighbor
statistics. Figures 13a-d show the distributions of the distance to the first
nearest neighbor (nnd) from YSOs to clumps and vice versa, with YSO
populations separated into Class 0/I protostars and Class II pre-main sequence
stars. Class-0/I and clumps are similarly distributed in the northern and
southern subregions (see Table 3 and Fig. 12), in addition the nnd
distributions between Class-0/I and clumps cannot be statistically
distinguished, according to a KS test. We therefore combined the northern and
southern populations of Class 0/I protostars to increase the sample size (18
objects). The 10 clumps detected in the _Herschel_ area not covered by
_Spitzer_ (see Fig. 12 and Table 3) and thus not surveyed for YSOs by Rapson
et al. (2014) have been excluded from our analysis.

The nearest neighbor distance, $nnd$, distribution between Class 0/I
protostars and clumps first illustrates the strong link between Class 0/I
protostars and their parental clump. Indeed, the large majority, $80-95\%$, of
the Class 0s/Is of each of the NGC 2264 subregions lie at a distance smaller
than the median outer radius of clumps, i.e. below 0.25 pc (see Fig. 13a).
Conversely, the $nnd$ distribution of clumps with respect to Class 0/I
protostars shows in Fig. 13b that $\sim$40% of the clumps in the central
subregion host, within their outer radius, at least one Class 0/I protostar.
This fraction raises to 2/3 when considering only bound clumps in the central
subregion. In addition to illustrating this parental link, the $nnd$
distributions between clumps and Class 0s/Is presents, for both the central
and northern $+$ southern samples a peak around 1 pc (see Fig. 13b). This peak
shows that aside the population of clumps tightly linked to Class 0/I, mostly
clustered in the central region, there exists all over NGC 2264 a disperse
population of clumps that are not associated to Class 0/I YSOs. As a matter of
fact, most of the clumps that create this peak, $\sim$75% whatever the
subregion, are qualified as unbound. These unbound clumps could be more than
transient cloud fragments because the external gas pressure associated with
the global collapse is not taken into account in the calculation of
gravitational boundedness. Excluding clumps presently qualified as unbound
however, the $nnd$ distribution between clumps and Class 0s/Is still displays
a peak, which is weaker and located at $\sim$0.5 pc.

Figures 13c-d display the $nnd$ distributions between clumps and Class II pre-
main sequence stars. Given the proper motions found by Buckner et al. (2020)
for the weakly embedded Class II sources in the NGC 2264 cloud, $\sim$1 mas
yr-1, and their estimated age, $\sim\times 10^{6}$ yr Venuti et al. (2018),
they could have dispersed by $\sim$6 pc from their original birth site. For
these Class II sources, we thus do not expect a direct parental link between
Class IIs and the presently observed clumps. Other Class II pre-main sequence
stars could well remain more tightly associated with their birth site, like it
is probably the case in the NGC 2264 IRS1 and IRS2 protoclusters. The
histograms of Figs. 13c-d should thus reveal the parental link of a clustered
population of Class IIs plus the average spatial distributions of a more
extended population of Class IIs, with no direct parental link with clumps. If
the small number, 19, of Class II YSOs in the southern subregion makes any
detailed characterization of the histogram shown in Fig. 13c not relevant,
that of Fig. 13d should be robust to interpret. While the $nnd$ distributions
between clumps and Class II sources are similar in the central and northern
subregions, the one of the southern subregion clearly stands out as different
(see Fig. 13d). The former are both broadly distributed around $\sim$0.2 pc.

In contrast, in the southern subregion, the $nnd$ distribution between clumps
and Class IIs presents a strong peak at $\sim$1 pc, which means at much
further distances than in the other parts of the cloud. This definitively is a
consequence of the low density of YSOs observed in the southern subregion (see
Fig. 12). One can question the association of Class IIs with the southern
subregion or even their membership in the NGC 2264 cloud. Indeed, with proper
motions like those measured by Buckner et al. (2020), the Class IIs observed
in the southern subregion could have formed in, and been ejected from, the
central subregion of NGC 2264. As a guideline, Buckner et al. (2020) also
estimated that up to 30% of the YSOs observed in the area covered by the NGC
2264 cloud may not be members of this star-forming region. A detailed
comparison of the star and cloud velocities measured with ground-based
radiotelescopes and the _gaia_ observatory would be necessary to evaluate the
YSO membership in the southern subregion of NGC 2264.

The comparison of the $nnd$ distributions of clumps with respect to YSOs (see
Figs. 13b and 13d), in the central and northern subregions, leads to an
apparent counter-intuitive result. On average, clumps are closer to Class II
pre-main sequence stars than to Class 0/I protostars: median $nnd$ of 0.2 pc
versus $\simeq$0.8 pc. This reflects the differences in the spatial
distribution visible in Fig. 12: while Class 0s/Is are, for their large
majority, clustered in the IRS1 and IRS2 protoclusters, Class IIs and clumps
display a more distributed population. This is totally inconsistent with
scenarios of quiet cloud formation followed by a continuous process of star
formation (see, e.g., Shu et al., 1987; Krumholz, 2014).

##  6 Discussion

We hereafter make the link between evidence for mass segregation observed for
NGC 2264 clumps and the cloud structure (see Section 6.1) and propose an
updated scenario for the star and thus cloud formation history in NGC 2264
(see Section 6.2).

###  6.1 Mass segregation of clumps and its relationship to cloud structure

Mass segregation has been studied extensively in stellar clusters for decades.
More recently, mass segregation has been investigated for cloud fragments with
0.002 pc to 0.1 pc sizes, using tools developed for stellar clusters. In
particular, Dib & Henning (2019) calculated the mass segregation ratio of
cores in four star-forming regions: Taurus, Aquila, Corona Australis, and
W43-MM1. They found no mass segregation in Taurus but a significant level of
mass segregation in the three other clouds: $\Lambda_{\rm MSR}$ up to 4-9 for
the 6–14 most massive cores. Plunkett et al. (2018) also reported that Serpens
South is mass segregated, with a median $\Lambda_{\rm MSR}\simeq 4$ for the
$N_{\rm MST}\leq 18$ most massive cores. Parker (2018) and Könyves et al.
(2020) in Orion B, Román-Zúñiga et al. (2019) in Orion A, Sadaghiani et al.
(2020) in NGC 6334 also claimed to find mass segregation, although with lower
values ($\Lambda_{\rm MSR}\simeq 2-3$) and/or involving a smaller number of
cores. Besides, no significant mass segregation was found in 12 infrared-dark
clouds by Sanhueza et al. (2019). As for the surface density parameter,
$\Sigma_{\rm j}$, sometimes used to quantify mass segregation, it was up to
now only computed for a couple of star-forming regions. Lane et al. (2016),
Parker (2018) and Dib & Henning (2019) notably reported tentative trends for
the most massive cores in Orion-A, Orion B, and Corona Australis to sit in
areas of higher local surface density. In particular, Lane et al. (2016) found
in Orion B a median $\Sigma_{\rm 10}$ value twice larger for the 10 most
massive cores than for the whole source samples.

When comparing these published results with the mass segregation found in NGC
2264 (see Section 4.5), the latter is among the strongest and affecting the
largest numbers of objects. The $\Lambda_{\rm MSR}$ parameter is indeed
measured to be $\Lambda_{\rm MSR}\simeq 8$ for the $N_{\rm MST}=4$ to 15 most
massive clumps (see Table 3 and Fig. 11a). Moreover, mass segregation remains
significant for the $\sim$30 most massive clumps of the NGC 2264 cloud. When
the mass segregation is measured by the local surface density, it has a median
$\Sigma_{6}$ value for the 15 most massive clumps four times larger than that
of the whole sample. We refrained making more quantitative comparisons with
published studies since the $\Lambda_{\rm MSR}$ values are measured with
different methods (e.g., sliding window in Román-Zúñiga et al. 2019; Könyves
et al. 2020 instead of cumulative form here) and datasets are inhomogeneous.
Sources were indeed extracted with different extraction tools (e.g., clumpfind
in Román-Zúñiga et al. 2019, FellWalker in Parker 2018 versus _getsf_ here)
and thus have various definitions. The cloud fragments considered also have
different physical sizes, which range from $\sim$0.002 pc (or $\sim$400 AU) in
Plunkett et al. (2018), 0.01-0.03 pc (or 2000-6000 AU) in Dib & Henning (2019)
to $\sim$0.1 pc (or $\sim$2$\times 10^{4}$ AU) in the present study. Moreover,
there are also issues, generally not taken into account, related to the
incompleteness of samples and crowding of sources in measuring the mass
segregation in dense environments like massive (proto)clusters (Ascenso et
al., 2009).

As shown in Fig. 11b, this strong mass segregation is entirely due to the
concentration of high-mass clumps in the NGC 2264-IRS1 and IRS2 protoclusters,
in the central subregion. We showed that these high-mass clumps all are
gravitationally bound and lie at short distance from each other (see Sections
4.3-4.4 and Fig. 3), thus forming a cluster of star-forming clumps. The
overabundance of high-mass clumps in the central subregion of NGC 2264
relative to the northern and southern subregions is consistent with its
greater gas concentration. After removing clumps from the column density image
of Fig. 2, the average column density of this background, at the location of
clumps, is indeed almost twice higher in the central subregion than in the
other NGC 2264 subregions (see Table 2). The relation between the mass of
cloud fragments and their surrounding gas has been reported for several star-
forming regions (e.g., Könyves et al., 2020) and presents here a linear
correlation between the mass of NGC 2264 clumps, $M$, and the column density
of their surrounding background, $N_{\rm H_{2}}^{\rm back}$ (see Fig. A5). The
incompleteness level of the clump sample could however partly explain this
linear correlation and especially the lack of low-mass clumps over regions
with high background level.

The overabundance of high-mass clumps in the central subregion is also in line
with the fact that its hierarchical structure is different from that of the
northern and southern subregions (see Section 3.2). Indeed, the power spectrum
of the central subregion, which is hundred times stronger, in fact consists of
the sum of a powerlaw function plus power excesses at the scales of cloud and
clump mass reservoirs (see Fig. 6b and Table 2). While diffuse clouds and
Gould Belt clouds generally have simple multi-fractal power spectra
(Robitaille et al., 2019, 2020), those of high-mass star-forming regions are
more complex and dominated, at given scales, by large gravity potentials such
as hubs and ridges (Robitaille et al. in prep.). Following the definition
criteria set in HOBYS articles (e.g., Hill et al., 2011; Hennemann et al.,
2012; Nguyen Luong et al., 2013) and precursor papers (Schneider et al.,
2010), the central part of the central subregion of NGC 2264 indeed qualifies
as a ridge. In short, ridges are very dense, $>$105 cm-3, $\sim$1 pc cloud
structures actively forming clusters of intermediate- to high-mass stars
(Motte et al., 2018). The center of NGC 2264 indeed hosts a dense north-south
filament, which appears as a privileged site for intermediate- to high-mass
star formation (see Figs. 3 and 12). It contains a cluster of massive clumps
hosting protostars, which could ultimately form a rich YSO cluster with at
least a handful of high-mass stars. The mass segregation observed for the NGC
2264 clumps could thus be at the origin of the mass segregation of future
stars in, at least, the central stellar cluster of NGC 2264.

We postulate that this mass segregation could come from further afield, in the
very way the mass of gas was assembled to form the molecular cloud in the NGC
2264 region. The large-scale kinematics observed throughout the Monoceros
cloud complex (Loren, 1977; Montillaud et al., 2019) and the global infall
discovered toward the NGC 2264-IRS1 protocluster (Williams & Garland, 2002;
Peretto et al., 2006) indeed suggest that a hierarchical collapse of the NGC
2264 cloud could have led to the formation of a ridge at its center. Further
studies of the cloud kinematics are necessary to investigate whether the three
filaments that appear converging toward the central ridge, drive material and
therefore feed the ridge and its protoclusters. This mode of cloud and thus
star formation by competitive, inflowing gas is advocated in theories of
hierarchical global collapse (e.g., Vázquez-Semadeni et al., 2019),
gravitationally-driven gas inflows (e.g., Smith et al., 2009; Hartmann et al.,
2012), filament or conveyor-belt collapse (Myers, 2009; Krumholz & McKee,
2020) and colliding flows (e.g., Heitsch et al., 2008).

###  6.2 Star formation history in NGC 2264

Evidence for sequential star formation in NGC 2264 has been presented by
several YSO studies (Sung & Bessell, 2010; Teixeira et al., 2012; Rapson et
al., 2014; Venuti et al., 2018). Venuti et al. (2018) proposed that star
formation began $\sim$5 Myr ago in the northern subregion and more precisely
in the S Mon area. Then, less than 1 Myr ago, star formation could have
developed in the central subregion and especially in the NGC 2264-IRS1 and
IRS2 protoclusters. In the present study, this age gradient is qualitatively
confirmed when comparing YSO distributions of the northern and central
subregions. The central subregion indeed concentrates about 80% of the total
Class 0/I protostars of the NGC 2264 cloud while the northern subregion is
mainly populated by Class II pre-main sequence stars (see Table 3 and Section
5.1). As for the southern subregion of the NGC 2264, it has not been
considered in YSO studies because it contains very few YSOs, only 6% of the
total population, suggesting that star formation has not yet been active
there. In order to more accurately compare subregions, Fig. 14 displays, for
each YSO (Class II and Class 0/I) population, their surface density divided by
their statistical lifetime. Surface densities, $\Sigma_{\rm object}$, are
calculated using the number of objects in each YSO class and the subregion
surfaces listed in Tables 2 and 3. We assumed lifetimes, $\tau_{\rm object}$,
of $2\times 10^{5}$ yr and $2\times 10^{6}$ yr for Class I protostars and
Class II pre-main sequence stars, respectively, in agreement with Evans et al.
(2009), Venuti et al. (2018) and references therein. Error bars on
$\Sigma_{\rm object}/\tau_{\rm object}$ take into account statistical
uncertainties, as well as an estimation of potentially undetected Class 0/Is
in the central region and field contamination of Class II population (see
Section 5.2 and Buckner et al., 2020). The surface densities per lifetime of
Class IIs and Class 0/Is are similar in the northern subregion. This behavior
is consistent with the idea that a continuous star formation activity has
developed in the northern part of NGC 2264 over the past few $10^{6}$ years.
In contrast, in the central and southern subregions, the surface densities per
lifetime are $3-5$ times larger for Class 0/I protostars than those measured
for Class IIs, suggesting an increase of the star formation activity over the
past few $10^{5}$ years.

Figure 14: Surface density per lifetime, $\Sigma_{\rm object}/\tau_{\rm
object}$, of Class II YSOs, Class 0/I protostars, and clumps in the three NGC
2264 subregions. Statistical lifetimes of 2, 0.2 and 0.3 Myr were assumed for
Class IIs, Class 0/Is, and clumps, respectively. Error bars take into account
statistical uncertainties, contamination level of 30% for Class IIs, an
incompleteness level of 25% for the Class 0/I population in the central
subregion, and a factor of two of uncertainty for the clumps. While the star
formation activity of the northern subregion appears to be constant, it
displays a burst in the central subregion and shows a tentative increase in
the southern subregion.

Identifying and characterizing the $\sim$0.1 pc clumps in NGC 2264, using its
_Herschel_ column density image, allows, for the first time, to study the gas
mass reservoir for current and future star formation in the NGC 2264 cloud. In
particular, bound clumps represent structures that are forming or will likely
form stars in the near future. The clump lifetime is not as properly defined
as the one of YSOs but, with a median density of $\sim\,8\times 10^{3}$ cm-3,
they could live for one free-fall time, i.e. $\sim\,3\times 10^{5}$ yrs. With
the goal to further characterize the relative star formation activity in the
NGC 2264 cloud, Fig. 14 displays the surface density per lifetime of bound
clumps, as measured in the three subregions of NGC 2264. We estimate that the
$\Sigma_{\rm clump}/\tau_{\rm clump}$ values are uncertain by a factor of at
least two. Indeed, as the median densities of clumps vary by a factor of 2
depending on the subregions, the clump free-fall time is thus uncertain by a
factor of $\sim\,50\leavevmode\nobreak\ \%$. Other sources of uncertainty are
the number of protostars that will form in each clump, here taken as one, and
the number of free-fall times, here assumed to be one, required for these
clumps to form stars. With our current assumptions, the surface density per
lifetime of clumps is similar to that of YSOs in the northern subregion,
implying a continuous star formation activity. In contrast in the southern
subregion, Fig. 14 suggests a tentative increase of the surface density per
lifetime of clumps compared to that of the YSOs and therefore a possible
increase of the star formation activity from $10^{6}$ years ago to the next
few $10^{5}$ years. In the central subregion, uncertainties are too large to
conclude that there is any trend for the future star formation activity
relative to the current one. All in one, the spatial distribution of YSO and
clump populations suggests that the star formation activity will get more and
more intense as we go through the NGC 2264 cloud, from north to south, in the
coming $\sim 3\times 10^{5}$ years. Star formation first developed mainly in
the northern subregion and continues today. Star formation is now extremely
active in the central subregion while it was less so in the past. Star
formation now begins in the southern subregion and could get more active while
it was almost nonexistent in the past. This time sequence, based on the study
of clump and YSO populations, agrees and extends the evolutionary sequence
proposed by, e.g., Venuti et al. (2018).

We propose here that this sequence of star formation is related to a prior
sequence of cloud formation, first in the north, now in the center and in the
future in the south of NGC 2264. With its large mass reservoir at pc scale
(see Table 2), the central subregion, and especially its ridge, has a very
large potential for new events of star formation. The cloud power spectrum of
the central subregion, and especially its coherent component associated to
star formation, also implies that the cloud reservoir for star formation will
remain important or will even increase in the coming $10^{6}$ years (see Fig.
6b). Indeed, the coherent component of the cloud contains almost all the power
and thus most of the gas mass reservoir in the central subregion (see Figs.
6a-b, Fig. 14 and Table 2). Interestingly, this fraction of coherent cloud
structures supposedly associated with star formation is orders of magnitude
larger in the central subregion of NGC 2264 than in the northern and southern
subregions. The southern subregion has similar properties with respect to its
gas distribution (column density and power spectrum) than the northern
subregion (see Fig. 6b and Table 3). These observations of the gas thus show
that the northern and southern subregions should have the same potential for
future star formation events, i.e. similar numbers of stars that could form in
the coming $\sim\,10^{6}$ years. This is also consistent with the similarity
observed in term of mass, boundedness, and surface density (see Fig. 14 and
Table 3). We therefore propose that the NGC 2264 cloud initially concentrated
in the northern region several Myrs ago. The cloud distribution in the central
subregion makes a strong case for extremely active cloud formation activity
now and possibly in the coming $10^{6}$ years. As for the southern subregion,
cloud is not yet highly concentrated but it could be enhanced in the close
future. Kinematical studies of the the NGC 2264 cloud are obviously necessary
to confirm this last assertion.

Over the past few Myr, the NGC 2264 cloud has thus undergone several episodes
of cloud and thus star formation. We showed in Section 5.2 that a large
fraction of clumps in the NGC 2264-IRS1 and IRS2 protoclusters are the
parental cloud structures from which the Class 0/I protostars currently gather
their mass. In addition, a population of clumps is observed at large, $\sim$1
pc, distances from the current sites of star-formation activity (see Fig. 12
and Fig. 13b). These clumps are smaller in mass and mostly unbound. They thus
need to gather more mass and concentrate themselves before they could be
considered as the cloud structures that will form new protostars. Future star-
formation episodes could develop either aside the current star-formation sites
or clumps must first be driven toward them. The population of clumps also
appears to be spatially well correlated with Class II pre-main sequence stars,
surprisingly better than with Class 0/I protostars. These elements argue for a
dynamical scenario of star formation in NGC 2264. We speculate that, first and
simultaneously, the cloud undergoes global collapse and clumps converge toward
the central gravity potentials, called ridges, reducing their distance to
star-forming sites. Protostellar collapse would then form a cluster of stars
with a definite mass segregation at birth. The last phase would then be for at
least part of the population of pre-main sequence stars to disperse from their
original birth sites and spread over the entire extent of the NGC 2264 cloud,
thus simulating a tight spatial correlation with a new generation of clumps.
Whether the most massive and most clustered protostars, embedded within cloud
ridges will become closely-packed main sequence stars will define if mass
segregation is determined at birth in NGC 2264 and potentially in other
dynamical clouds. This scenario is consistent with the finding of Buckner et
al. (2020) that YSOs with increasing evolutionary stages tend to be less
clustered. It would also qualitatively agree with numerical simulations of
cloud formation through, e.g., the hierarchical global collapse (Vázquez-
Semadeni et al., 2019) and stellar cluster evolution with the ejection of many
YSOs members (Oh & Kroupa, 2016).

##  7 Conclusions

We used an _Herschel_ column density image of the NGC 2264 cloud to identify
and characterize its clumps, study their spatial distribution, and make the
link of these clumps with the YSO population and cloud structure. Our main
results can be summarized as follows:

  1. 1.

We applied getsf to the 18.2$\arcsec$-angular resolution column density image.
We found a population of 256 clumps with $\sim$0.1 pc sizes, $\sim$14 K
temperature and masses ranging from 0.08 $M_{\odot}$ to 53 $M_{\odot}$. 36% of
the NGC 2264 clumps are gravitationally bound following the Bonnor-Ebert
criterium.

  2. 2.

After analyzing the cloud structure, we divided the NGC 2264 cloud into three
subregions (northern, central, and southern) of roughly similar areas. The
MnGseg technique revealed that the coherent cloud component associated to star
formation dominates over the Gaussian component, especially for the central
subregion. We qualified as a ridge the cloud structure at the center of the
central subregion of NGC 2264 because it is massive and hosts the NGC
2264-IRS1 and IRS2 protoclusters.

  3. 3.

We then applied the $nnd$ statistics to characterize the spatial distribution
of clumps in the NGC 2264 cloud. The majority of the clumps are low-mass,
unbound structures distributed all over the cloud. In addition, we found
within the NGC 2264 ridge a cluster of higher-mass, bound clumps. The typical
distance between clumps in this second population is half that found
elsewhere.

  4. 4.

We quantified the mass segregation of NGC 2264 clumps using two complementary
methods. The 15 most massive clumps, with $M=9.3-53\leavevmode\nobreak\
M_{\odot}$ and all located in the central protoclusters, have a median local
surface density, $\Sigma_{6}$, three times that of the entire clump
population. This high degree of mass segregation is also indicated by the
strong mass segregation ratio: $\Lambda_{\rm MSR}\simeq 8$ for the $N_{\rm
MST}\leq 15$ most massive clumps. We propose that this mass segregation is due
to the presence and therefore to the formation of a ridge by the global
collapse of clouds in the central subregion of NGC 2264.

  5. 5.

Except for those in the ridge, most of the clumps in the NGC 2264 cloud are
not associated with Class 0/I protostars. At odd with what a parental link
would suggest, $nnd$ distributions notably show that the spatial distribution
of clumps better correlates with that of Class II pre-main sequence stars.
This argues for a dynamical scenario where clumps are driven to the central
parts of the NGC 2264, protostars are formed in situ, and some Class IIs are
ejected from their birth site.

  6. 6.

Our combined study of clump and YSO populations in NGC 2264 agrees and extends
the sequential star formation sequence from north to south, which was
initially proposed by YSO studies. Star formation first developed in the
northern subregion. It is now extremely active in the central subregion and
should soon begin in the southern subregion. We propose to explain it by a
sequence of cloud formation from north to south.

The strong mass segregation observed for the NGC 2264 clumps, which is most
likely due to the formation of a favorable cloud structure, the ridge, could
itself be at the origin of future mass segregation in the NGC 2264 stellar
cluster. Detailed studies of the cloud and YSO kinematics are mandatory to
confirm our current interpretation and predictions.

###### Acknowledgements.

This project has received funding from the European Union’s Horizon 2020
research and innovation programs StarFormMapper under grant agreement No
687528 and Filaments-to-stars under the Marie Skłodowska-Curie Grant Agreement
No. 750920. This project was also supported by the Programme National de
Physique Stellaire and Physique et Chimie du Milieu Interstellaire (PNPS and
PCMI) of CNRS/INSU (with INC/INP/IN2P3) co-funded by CEA and CNES. This
research has made use of data from the Herschel imaging survey of OB young
stellar objects (HOBYS) project (http:// hobys-herschel.cea.fr). HOBYS is a
_Herschel_ Key Program jointly carried out by SPIRE Specialist Astronomy Group
3 (SAG 3), scientists of several institutes in the PACS Consortium (LAM
Marseille and CEA Saclay), and scientists of the Herschel Science Center
(HSC).

## References

  * Allison et al. (2009) Allison, R. J., Goodwin, S. P., Parker, R. J., et al. 2009, MNRAS, 395, 1449
  * André et al. (2010) André, P., Men’shchikov, A., Bontemps, S., et al. 2010, A&A, 518, L102
  * Ascenso et al. (2009) Ascenso, J., Alves, J., & Lago, M. T. V. T. 2009, A&A, 495, 147
  * Buckner et al. (2020) Buckner, A. S. M., Khorrami, Z., González, M., et al. 2020, A&A, 636, A80
  * Cantat-Gaudin et al. (2018) Cantat-Gaudin, T., Jordi, C., Vallenari, A., et al. 2018, A&A, 618, A93
  * Cartwright & Whitworth (2004) Cartwright, A. & Whitworth, A. P. 2004, MNRAS, 348, 589
  * Casertano & Hut (1985) Casertano, S. & Hut, P. 1985, ApJ, 298, 80
  * Cody et al. (2014) Cody, A. M., Stauffer, J., Baglin, A., et al. 2014, AJ, 147, 82
  * Cunningham et al. (2016) Cunningham, N., Lumsden, S. L., Cyganowski, C. J., Maud, L. T., & Purcell, C. 2016, MNRAS, 458, 1742
  * Dahm (2008) Dahm, S. E. 2008, The Young Cluster and Star Forming Region NGC 2264, ed. B. Reipurth, Vol. 4, 966
  * Dib & Henning (2019) Dib, S. & Henning, T. 2019, A&A, 629, A135
  * Dib et al. (2018) Dib, S., Schmeja, S., & Parker, R. J. 2018, MNRAS, 473, 849
  * Didelon et al. (2015) Didelon, P., Motte, F., Tremblin, P., et al. 2015, A&A, 584, A4
  * Evans et al. (2009) Evans, Neal J., I., Dunham, M. M., Jørgensen, J. K., et al. 2009, ApJS, 181, 321
  * Fujii & Portegies Zwart (2016) Fujii, M. S. & Portegies Zwart, S. 2016, ApJ, 817, 4
  * Griffin et al. (2010) Griffin, M. J., Abergel, A., Abreu, A., et al. 2010, A&A, 518, L3
  * Gutermuth et al. (2009) Gutermuth, R. A., Megeath, S. T., Myers, P. C., et al. 2009, ApJS, 184, 18
  * Hartmann et al. (2012) Hartmann, L., Ballesteros-Paredes, J., & Heitsch, F. 2012, MNRAS, 420, 1457
  * Heitsch et al. (2008) Heitsch, F., Hartmann, L. W., Slyz, A. D., Devriendt, J. E. G., & Burkert, A. 2008, ApJ, 674, 316
  * Hennemann et al. (2012) Hennemann, M., Motte, F., Schneider, N., et al. 2012, A&A, 543, L3
  * Hetem & Gregorio-Hetem (2019) Hetem, A. & Gregorio-Hetem, J. 2019, MNRAS, 490, 2521
  * Hill et al. (2011) Hill, T., Motte, F., Didelon, P., et al. 2011, A&A, 533, A94
  * Hill et al. (2012) Hill, T., Motte, F., Didelon, P., et al. 2012, A&A, 542, A114
  * Juvela et al. (2012) Juvela, M., Ristorcelli, I., Pagani, L., et al. 2012, A&A, 541, A12
  * Kirby (2005) Kirby, J. F. 2005, Computers and Geosciences, 31, 846
  * Könyves et al. (2020) Könyves, V., André, P., Arzoumanian, D., et al. 2020, A&A, 635, A34
  * Krumholz (2014) Krumholz, M. R. 2014, Phys. Rep, 539, 49
  * Krumholz & McKee (2020) Krumholz, M. R. & McKee, C. F. 2020, MNRAS, 494, 624
  * Kuhn et al. (2014) Kuhn, M. A., Feigelson, E. D., Getman, K. V., et al. 2014, ApJ, 787, 107
  * Lane et al. (2016) Lane, J., Kirk, H., Johnstone, D., et al. 2016, ApJ, 833, 44
  * Loren (1977) Loren, R. B. 1977, ApJ, 215, 129
  * Maschberger & Clarke (2011) Maschberger, T. & Clarke, C. J. 2011, MNRAS, 416, 541
  * Maury et al. (2009) Maury, A. J., André, P., & Li, Z. Y. 2009, A&A, 499, 175
  * Men’shchikov (2013) Men’shchikov, A. 2013, A&A, 560, A63
  * Men’shchikov (2017) Men’shchikov, A. 2017, A&A, 607, A64
  * Men’shchikov et al. (2012) Men’shchikov, A., André, P., Didelon, P., et al. 2012, A&A, 542, A81
  * Meylan (2000) Meylan, G. 2000, in Astronomical Society of the Pacific Conference Series, Vol. 211, Massive Stellar Clusters, ed. A. Lançon & C. M. Boily, 215
  * Montillaud et al. (2019) Montillaud, J., Juvela, M., Vastel, C., et al. 2019, A&A, 631, A3
  * Motte et al. (2018) Motte, F., Bontemps, S., & Louvet, F. 2018, ARA&A, 56
  * Motte et al. (2010) Motte, F., Zavagno, A., Bontemps, S., et al. 2010, A&A, 518, L77
  * Murray & Lin (1996) Murray, S. D. & Lin, D. N. C. 1996, ApJ, 467, 728
  * Myers (2009) Myers, P. C. 2009, ApJ, 700, 1609
  * Nguyen Luong et al. (2013) Nguyen Luong, Q., Motte, F., Carlhoff, P., et al. 2013, ApJ, 775, 88
  * Nguyen Luong et al. (2011) Nguyen Luong, Q., Motte, F., Hennemann, M., et al. 2011, A&A, 535, A76
  * Oh & Kroupa (2016) Oh, S. & Kroupa, P. 2016, A&A, 590, A107
  * Ott (2010) Ott, S. 2010, in Astronomical Society of the Pacific Conference Series, Vol. 434, Astronomical Data Analysis Software and Systems XIX, ed. Y. Mizumoto, K. I. Morita, & M. Ohishi, 139
  * Palmeirim et al. (2013) Palmeirim, P., André, P., Kirk, J., et al. 2013, A&A, 550, A38
  * Parker (2018) Parker, R. J. 2018, MNRAS, 476, 617
  * Parker & Goodwin (2015) Parker, R. J. & Goodwin, S. P. 2015, MNRAS, 449, 3381
  * Parker et al. (2016) Parker, R. J., Goodwin, S. P., Wright, N. J., Meyer, M. R., & Quanz, S. P. 2016, MNRAS, 459, L119
  * Parker & Meyer (2012) Parker, R. J. & Meyer, M. R. 2012, MNRAS, 427, 637
  * Peretto et al. (2006) Peretto, N., André, P., & Belloche, A. 2006, A&A, 445, 979
  * Peretto et al. (2013) Peretto, N., Fuller, G. A., Duarte-Cabral, A., et al. 2013, A&A, 555, A112
  * Plunkett et al. (2018) Plunkett, A. L., Fernández-López, M., Arce, H. G., et al. 2018, A&A, 615, A9
  * Poglitsch et al. (2010) Poglitsch, A., Waelkens, C., Geis, N., et al. 2010, A&A, 518, L2
  * Povich et al. (2013) Povich, M. S., Kuhn, M. A., Getman, K. V., et al. 2013, ApJS, 209, 31
  * Rapson et al. (2014) Rapson, V. A., Pipher, J. L., Gutermuth, R. A., et al. 2014, ApJ, 794, 124
  * Rayner et al. (2017) Rayner, T. S. M., Griffin, M. J., Schneider, N., et al. 2017, A&A, 607, A22
  * Rivera-Ingraham et al. (2013) Rivera-Ingraham, A., Martin, P. G., Polychroni, D., et al. 2013, ApJ, 766, 85
  * Robitaille et al. (2020) Robitaille, J.-F., Abdeldayem, A., Joncour, I., et al. 2020, arXiv e-prints, arXiv:2007.08206
  * Robitaille et al. (2014) Robitaille, J.-F., Joncas, G., & Miville-Deschênes, M.-A. 2014, MNRAS, 440, 2726
  * Robitaille et al. (2019) Robitaille, J. F., Motte, F., Schneider, N., Elia, D., & Bontemps, S. 2019, A&A, 628, A33
  * Román-Zúñiga et al. (2019) Román-Zúñiga, C. G., Alfaro, E., Palau, A., et al. 2019, MNRAS, 489, 4429
  * Roussel (2013) Roussel, H. 2013, PASP, 125, 1126
  * Sadaghiani et al. (2020) Sadaghiani, M., Sánchez-Monge, Á., Schilke, P., et al. 2020, A&A, 635, A2
  * Sánchez & Alfaro (2009) Sánchez, N. & Alfaro, E. J. 2009, ApJ, 696, 2086
  * Sanhueza et al. (2019) Sanhueza, P., Contreras, Y., Wu, B., et al. 2019, ApJ, 886, 102
  * Schneider et al. (2010) Schneider, N., Csengeri, T., Bontemps, S., et al. 2010, A&A, 520, A49
  * Schneider et al. (2012) Schneider, N., Csengeri, T., Hennemann, M., et al. 2012, A&A, 540, L11
  * Schwartz (1987) Schwartz, P. R. 1987, ApJ, 320, 258
  * Shu et al. (1987) Shu, F. H., Adams, F. C., & Lizano, S. 1987, ARA&A, 25, 23
  * Smith et al. (2009) Smith, R. J., Longmore, S., & Bonnell, I. 2009, MNRAS, 400, 1775
  * Spitzer (1969) Spitzer, Lyman, J. 1969, ApJ, 158, L139
  * Sung & Bessell (2010) Sung, H. & Bessell, M. S. 2010, AJ, 140, 2070
  * Sung et al. (2008) Sung, H., Bessell, M. S., Chun, M.-Y., Karimov, R., & Ibrahimov, M. 2008, AJ, 135, 441
  * Sung et al. (2009) Sung, H., Stauffer, J. R., & Bessell, M. S. 2009, AJ, 138, 1116
  * Teixeira et al. (2012) Teixeira, P. S., Lada, C. J., Marengo, M., & Lada, E. A. 2012, A&A, 540, A83
  * Teixeira et al. (2006) Teixeira, P. S., Lada, C. J., Young, E. T., et al. 2006, ApJ, 636, L45
  * Tigé et al. (2017) Tigé, J., Motte, F., Russeil, D., et al. 2017, A&A, 602, A77
  * Vázquez-Semadeni et al. (2019) Vázquez-Semadeni, E., Palau, A., Ballesteros-Paredes, J., Gómez, G. C., & Zamora-Avilés, M. 2019, MNRAS, 490, 3061
  * Venuti et al. (2018) Venuti, L., Prisinzano, L., Sacco, G. G., et al. 2018, A&A, 609, A10
  * Williams & Garland (2002) Williams, J. P. & Garland, C. A. 2002, ApJ, 568, 259
  * Young et al. (2006) Young, E. T., Teixeira, P. S., Lada, C. J., et al. 2006, ApJ, 642, 972

Figure A1: _Herschel_ dust temperature image of the NGC 2264 cloud, with
$3.4\times 10^{21}$ H2 cm-2 and $1.6\times 10^{22}$ H2 cm-2 contours taken
from the column density image of Fig. 2. The resolution of the map, 18.2″, is
shown in the lower-right corner and a scale bar is given in the upper-left
corner. A white star locates the OB star cluster which heats its surrounding
cloud over several parsecs. The IRS1 and IRS2 protoclusters are also
indicated.

Figure A2:  Completeness level of the clumps extraction as a function of the
clump mass (in a) and of the contrast of the clump column density over its
local background (in b). The 90% completeness level is indicated with a dashed
horizontal line. a) The clump sample is 90% complete above 1.7 $M_{\odot}$. b)
The histogram has been constructed with a constant number of 80 synthetic
sources in the 11 first bins, plus a bin containing the remaining 290 sources
which have all been detected. The red curve connects the median contrast value
in each bin. 90% of the clump are detected for a contrast larger than 1.1.

Figure A3:  In relation with the intensity profile of clumps, clump masses are
plotted against the peak column density a) for synthetic sources and b) for
observed clumps. A simple regression gives the relation $M=\beta(N_{\rm
H2}^{\rm peak})^{0.95}$ with $\beta=0.37$ in a over a [$0.6-12\times 10^{21}$
cm-2] range, $\beta=0.34$ in b over a [$0.5-200\times 10^{21}$ cm-2]. The
similarity in the fitted parameters confirms that the synthetic sources
correctly represent the observed clumps.  Figure A4: Temperature distribution
of the 256 clumps detected in NGC 2264. The temperature dispersion between
clumps in small, 80% of the clumps having a temperature between 12.7 and 16.9
K Figure A5: A correlation is observed between the clump mass and its
background column density. The color code highlighting the spatial location of
the clumps is the same as in Fig. 7. The completeness curve in dotted line is
calculated using a 90% completeness contrast of 1.1 and the M-$N_{\rm H2}^{\rm
peak}$ relation (see Figs. A2 and A3): $M=0.34\,(1.1\times N_{\rm H2}^{\rm
back})^{0.95}$ Table A4: Catalog of clumps.

# Alp Del $A\times B$ PA Sig $N^{\rm peak}_{\rm H_{2}}$ $N^{\rm int}_{\rm
H_{2}}$ FWHMdecbbfootnotemark: $b$ $T_{\rm dust}$ $M$ $\alpha_{\rm BE}$
$N^{\rm back}_{\rm H_{2}}$ [J2000] [J2000] [$"\times"$] [$\degree$] [Av
units]aafootnotemark: $a$ [Av units]aafootnotemark: $a$ [pc] [K] [$M_{\odot}$]
[Av units]aafootnotemark: $a$ 1 100.5263 9.1742 $18.2\times 18.2$ 18.3 80.5
$18\pm 2$ $14\pm 2$ 0.03 $20\pm 2$ $1.8\pm 0.2$ 0.3 $11.5\pm 0.2$ 2 100.2023
9.5775 $36.6\times 32.4$ 152.2 174.4 $80\pm 6$ $286\pm 10$ 0.1 $11.8\pm 0.3$
$30\pm 1$ 0.03 $17.4\pm 0.6$ 3 100.2944 9.9336 $27.4\times 26.2$ 24.5 104.8
$19\pm 2$ $41\pm 2$ 0.07 $15.7\pm 0.7$ $4.3\pm 0.2$ 0.21 $8.8\pm 0.2$ 4
99.8894 9.5995 $33.6\times 23.2$ 44.3 94.8 $30\pm 3$ $68\pm 4$ 0.07 $11.5\pm
0.4$ $7.0\pm 0.4$ 0.1 $13.0\pm 0.3$ 5 100.2081 9.0423 $30.1\times 23.4$ 168.7
76.2 $11.2\pm 0.8$ $21.0\pm 0.9$ 0.07 $12.3\pm 0.2$ $2.2\pm 0.1$ 0.31 $6.5\pm
0.1$ 6 100.2997 9.4868 $26.5\times 23.8$ 93.5 64.4 $209\pm 12$ $400\pm 15$
0.06 $17.3\pm 0.7$ $41\pm 2$ 0.02 $61\pm 1$ 7 100.2741 9.5998 $31.3\times
26.3$ 7.2 56.5 $82\pm 11$ $195\pm 14$ 0.08 $15.0\pm 0.4$ $20\pm 1$ 0.05 $46\pm
3$ 8 100.272 9.5686 $22.8\times 22.8$ 22.6 47.7 $77\pm 11$ $90\pm 13$ 0.05
$15.4\pm 0.7$ $9\pm 1$ 0.07 $52\pm 2$ 9 100.3104 9.4536 $29.5\times 20.2$ 48.4
50.1 $44\pm 8$ $77\pm 9$ 0.06 $13.3\pm 0.4$ $8.0\pm 0.9$ 0.08 $22.6\pm 0.6$ 10
100.5549 9.0945 $31.8\times 22.5$ 115.5 42.3 $11\pm 2$ $22\pm 3$ 0.07 $12.6\pm
0.4$ $2.3\pm 0.3$ 0.31 $9.7\pm 0.5$ 11 99.9603 9.6866 $40.8\times 24.9$ 93.2
45.8 $13\pm 1$ $35\pm 1$ 0.09 $12.2\pm 0.2$ $3.6\pm 0.1$ 0.26 $6.4\pm 0.2$ 12
99.9953 9.779 $38.2\times 25.4$ 56.7 46.9 $5.9\pm 0.2$ $17.0\pm 0.3$ 0.09
$13.5\pm 0.3$ $1.7\pm 0.0$ 0.58 $2.9\pm 0.0$ 13 100.2838 9.4998 $41.0\times
32.3$ 153.3 48.6 $138\pm 13$ $514\pm 21$ 0.11 $16\pm 2$ $53\pm 2$ 0.03 $41\pm
3$ 14 100.25 9.7985 $31.9\times 27.8$ 129.0 40.5 $5.9\pm 0.9$ $15\pm 1$ 0.08
$15.1\pm 0.3$ $1.6\pm 0.1$ 0.65 $7.1\pm 0.2$ 15 100.224 8.9245 $38.6\times
28.0$ 174.2 44.6 $5.3\pm 0.4$ $16.0\pm 0.6$ 0.1 $13.0\pm 0.2$ $1.7\pm 0.1$
0.61 $3.9\pm 0.1$ 16 100.2746 9.5619 $46.7\times 26.6$ 174.2 32.2 $67\pm 11$
$204\pm 15$ 0.11 $13.7\pm 0.7$ $21\pm 2$ 0.06 $45\pm 3$ 17 100.2961 9.5946
$37.4\times 25.9$ 166.2 31.2 $46\pm 13$ $100\pm 13$ 0.09 $14.0\pm 0.9$ $10\pm
1$ 0.1 $35\pm 3$ 18 100.1488 9.9097 $23.8\times 22.8$ 113.5 31.8 $5\pm 1$
$9\pm 2$ 0.05 $15.8\pm 0.4$ $0.9\pm 0.2$ 0.74 $5.2\pm 0.3$ 19 100.1259 9.8254
$37.1\times 24.9$ 155.3 39.4 $6\pm 1$ $16\pm 2$ 0.09 $16.5\pm 0.3$ $1.7\pm
0.2$ 0.69 $5.9\pm 0.2$ 20 100.3121 9.4895 $57.4\times 27.1$ 25.2 36.5 $105\pm
13$ $382\pm 19$ 0.12 $14.3\pm 0.4$ $40\pm 2$ 0.04 $55\pm 3$ 21 99.9869 9.7646
$46.5\times 29.6$ 15.4 36.3 $4.4\pm 0.3$ $16.0\pm 0.5$ 0.11 $13.3\pm 0.3$
$1.7\pm 0.1$ 0.73 $2.9\pm 0.1$ 22 100.2898 9.4898 $33.9\times 26.9$ 47.0 26.1
$120\pm 13$ $280\pm 14$ 0.08 $22\pm 1$ $29\pm 2$ 0.05 $54\pm 2$ 23 100.1215
9.9109 $48.7\times 26.4$ 63.9 45.7 $9\pm 1$ $31\pm 2$ 0.11 $15.0\pm 0.2$
$3.2\pm 0.2$ 0.42 $6.6\pm 0.2$ 24∗ 100.4425 8.868 $38.6\times 31.3$ 143.8 47.3
$4.8\pm 0.4$ $17.0\pm 0.8$ 0.1 $12.6\pm 0.2$ $1.8\pm 0.1$ 0.6 $3.1\pm 0.1$ 25
100.2921 9.3425 $61.6\times 33.6$ 59.7 54.6 $8\pm 1$ $39\pm 2$ 0.15 $13.4\pm
0.1$ $4.1\pm 0.2$ 0.4 $7.5\pm 0.3$ 26 100.238 9.6068 $40.9\times 34.9$ 82.4
38.8 $47\pm 13$ $186\pm 21$ 0.12 $13.6\pm 0.6$ $19\pm 2$ 0.07 $31\pm 4$ 27
100.3241 9.4849 $38.9\times 24.1$ 75.9 28.5 $49\pm 13$ $98\pm 15$ 0.09
$13.4\pm 0.3$ $10\pm 2$ 0.09 $42\pm 4$ 28 100.3172 9.6934 $42.1\times 29.6$
80.3 34.0 $8\pm 2$ $27\pm 3$ 0.11 $13.9\pm 0.2$ $2.8\pm 0.3$ 0.44 $10.0\pm
0.3$ 29 100.1688 8.9845 $48.3\times 28.3$ 73.7 40.7 $7.0\pm 0.9$ $27\pm 1$
0.11 $12.4\pm 0.1$ $2.8\pm 0.1$ 0.41 $7.4\pm 0.1$ 30 100.3801 9.449
$26.5\times 22.8$ 25.5 25.6 $2.5\pm 0.8$ $4.0\pm 0.9$ 0.06 $15.6\pm 0.3$
$0.4\pm 0.1$ 1.7 $3.8\pm 0.1$ 31 100.0337 9.6287 $39.7\times 31.8$ 36.3 38.8
$5.7\pm 0.9$ $21\pm 1$ 0.11 $14.7\pm 0.2$ $2.2\pm 0.1$ 0.59 $5.8\pm 0.1$ 32
99.9659 9.7352 $22.7\times 21.9$ 22.4 25.5 $2.6\pm 0.8$ $4.0\pm 0.9$ 0.05
$13.8\pm 0.2$ $0.4\pm 0.1$ 1.4 $3.3\pm 0.1$ 33 100.5634 8.9344 $46.5\times
44.0$ 95.3 50.8 $8.7\pm 0.7$ $53\pm 2$ 0.15 $12.4\pm 0.2$ $5.5\pm 0.2$ 0.27
$4.2\pm 0.2$ 34 100.1253 9.5813 $38.9\times 33.2$ 98.1 34.3 $6.4\pm 0.8$
$24\pm 1$ 0.11 $15.1\pm 0.3$ $2.5\pm 0.1$ 0.54 $7.4\pm 0.1$ 35 100.0074 9.7932
$43.4\times 28.3$ 65.5 27.8 $3.0\pm 0.2$ $8.0\pm 0.2$ 0.11 $14.2\pm 0.3$
$0.9\pm 0.0$ 1.4 $3.1\pm 0.1$ 36 100.2145 9.5727 $33.2\times 24.0$ 61.2 22.6
$18\pm 7$ $36\pm 8$ 0.08 $13.1\pm 0.3$ $3.8\pm 0.8$ 0.22 $21.9\pm 0.6$ 37
100.1184 9.8753 $33.0\times 31.5$ 18.7 28.9 $6\pm 1$ $16\pm 2$ 0.09 $17.8\pm
0.5$ $1.7\pm 0.2$ 0.81 $5.6\pm 0.2$ 38 100.2994 9.8134 $42.3\times 31.2$ 96.0
28.2 $7\pm 1$ $25\pm 2$ 0.11 $13.8\pm 0.2$ $2.6\pm 0.2$ 0.48 $8.3\pm 0.2$ 39
100.2034 9.2366 $54.4\times 31.1$ 86.2 38.4 $4.2\pm 0.5$ $20.0\pm 0.9$ 0.13
$13.0\pm 0.2$ $2.1\pm 0.1$ 0.66 $5.2\pm 0.1$ 40 100.2644 9.5847 $47.0\times
42.3$ 75.8 27.5 $66\pm 13$ $318\pm 19$ 0.14 $13.8\pm 0.4$ $33\pm 2$ 0.05
$54\pm 3$ 41 100.6272 8.9705 $43.1\times 25.2$ 38.8 34.5 $5.3\pm 0.5$ $16.0\pm
0.6$ 0.1 $12.6\pm 0.2$ $1.6\pm 0.1$ 0.62 $4.4\pm 0.2$ 42 100.4702 9.532
$19.4\times 19.4$ 4.4 19.8 $1.0\pm 0.1$ $1.0\pm 0.1$ 0.03 $13.8\pm 0.2$
$0.1\pm 0.0$ 3.0 $2.5\pm 0.0$ 43 100.0651 9.5435 $49.8\times 34.5$ 135.0 34.2
$3.3\pm 0.5$ $16.0\pm 0.8$ 0.13 $13.5\pm 0.2$ $1.7\pm 0.1$ 0.88 $4.2\pm 0.0$
44 100.0434 9.8478 $42.8\times 31.7$ 161.1 24.5 $3.3\pm 0.9$ $12\pm 1$ 0.11
$16.2\pm 0.3$ $1.2\pm 0.1$ 1.3 $4.2\pm 0.1$ 45 100.075 9.1437 $38.5\times
28.8$ 175.8 23.3 $3.3\pm 0.6$ $10.0\pm 0.9$ 0.1 $13.1\pm 0.1$ $1.1\pm 0.1$
0.99 $5.8\pm 0.1$ 46 100.2876 9.5489 $50.5\times 30.1$ 173.9 21.5 $42\pm 6$
$137\pm 8$ 0.12 $11.8\pm 0.2$ $14.1\pm 0.8$ 0.08 $31\pm 1$ 47 100.1391 9.8632
$22.8\times 22.8$ 116.8 18.3 $4\pm 1$ $5\pm 1$ 0.05 $19.8\pm 0.3$ $0.5\pm 0.1$
1.6 $6.5\pm 0.2$ 48 100.2183 9.8165 $23.6\times 20.1$ 70.1 17.4 $1.3\pm 0.6$
$2.0\pm 0.6$ 0.04 $22.7\pm 0.7$ $0.2\pm 0.1$ 4.7 $3.4\pm 0.1$ 49 100.0822
9.9404 $47.0\times 26.7$ 136.8 20.7 $7\pm 1$ $19\pm 1$ 0.11 $14.7\pm 0.2$
$2.0\pm 0.1$ 0.65 $5.1\pm 0.1$ 50 100.2993 9.4523 $31.3\times 24.7$ 175.5 18.4
$19\pm 5$ $34\pm 5$ 0.07 $13.2\pm 0.4$ $3.5\pm 0.6$ 0.23 $17.1\pm 0.7$ 51
100.1999 9.6023 $32.3\times 24.7$ 12.9 18.1 $10\pm 3$ $21\pm 3$ 0.08 $13.8\pm
0.4$ $2.2\pm 0.3$ 0.39 $13.4\pm 0.4$ 52 100.2532 8.9604 $57.3\times 31.9$ 4.4
20.2 $3.3\pm 0.6$ $14.0\pm 0.8$ 0.14 $13.3\pm 0.2$ $1.4\pm 0.1$ 1.1 $4.0\pm
0.2$ 53 100.1503 8.9688 $48.3\times 41.0$ 176.3 21.7 $6\pm 1$ $27\pm 2$ 0.14
$12.6\pm 0.1$ $2.8\pm 0.2$ 0.53 $6.3\pm 0.3$ 54 100.1049 9.0951 $34.1\times
26.5$ 43.2 20.4 $2.5\pm 0.8$ $7\pm 1$ 0.08 $13.2\pm 0.1$ $0.7\pm 0.1$ 1.3
$4.1\pm 0.2$ 55 100.3751 9.5917 $34.9\times 25.5$ 111.1 17.1 $3.9\pm 0.7$
$8.0\pm 0.7$ 0.08 $14.5\pm 0.2$ $0.8\pm 0.1$ 1.2 $5.7\pm 0.0$ 56 100.118
9.4395 $41.1\times 27.4$ 162.4 17.6 $4\pm 1$ $10\pm 2$ 0.1 $13.5\pm 0.3$
$1.1\pm 0.2$ 1.0 $5.4\pm 0.1$ 57 100.3073 9.7007 $55.8\times 39.1$ 66.0 23.9
$6\pm 2$ $36\pm 3$ 0.15 $14.0\pm 0.2$ $3.7\pm 0.3$ 0.47 $10.5\pm 0.4$ 58
100.2719 9.7925 $40.7\times 25.5$ 168.4 19.7 $4.6\pm 0.8$ $12\pm 1$ 0.09
$14.5\pm 0.1$ $1.3\pm 0.1$ 0.86 $8.4\pm 0.2$ 59 100.2287 9.8397 $29.6\times
25.1$ 92.0 21.3 $1.8\pm 0.7$ $4\pm 1$ 0.07 $19.1\pm 0.5$ $0.4\pm 0.1$ 2.8
$4.7\pm 0.1$ 60 100.5452 9.2047 $42.5\times 27.9$ 129.7 17.7 $6\pm 3$ $16\pm
3$ 0.1 $12.7\pm 0.3$ $1.6\pm 0.3$ 0.67 $7.4\pm 0.6$ 61 100.4242 9.3669
$35.7\times 24.6$ 119.9 18.9 $4\pm 1$ $9\pm 1$ 0.08 $14.1\pm 0.1$ $1.0\pm 0.1$
0.96 $5.7\pm 0.1$ 62 100.3618 9.3753 $52.8\times 41.7$ 30.6 29.5 $14\pm 2$
$81\pm 4$ 0.15 $12.7\pm 0.2$ $8.4\pm 0.4$ 0.19 $11.2\pm 0.3$ 63 100.0883
9.4561 $34.2\times 27.1$ 151.5 20.9 $3\pm 1$ $8\pm 2$ 0.09 $13.4\pm 0.2$
$0.8\pm 0.2$ 1.2 $4.7\pm 0.2$ 64 100.2161 9.7366 $31.7\times 26.4$ 7.5 21.2
$1.4\pm 0.4$ $4.0\pm 0.6$ 0.08 $17.5\pm 0.2$ $0.4\pm 0.1$ 3.2 $3.9\pm 0.1$ 65
99.9776 9.9467 $37.4\times 24.4$ 153.7 18.3 $3.4\pm 0.8$ $8.0\pm 0.9$ 0.08
$14.5\pm 0.2$ $0.8\pm 0.1$ 1.2 $3.8\pm 0.1$

Table A1: Catalog of clumps (continued).

# Alp Del $A\times B$ PA Sig $N^{\rm peak}_{\rm H_{2}}$ $N^{\rm int}_{\rm
H_{2}}$ FWHMdecbbfootnotemark: $b$ $T_{\rm dust}$ $M$ $\alpha_{\rm BE}$
$N^{\rm back}_{\rm H_{2}}$ [J2000] [J2000] [$"\times"$] [$\degree$] [Av
units]aafootnotemark: $a$ [Av units]aafootnotemark: $a$ [pc] [K] [$M_{\odot}$]
[Av units]aafootnotemark: $a$ 66 99.991 9.5787 $31.2\times 26.4$ 8.5 20.1
$1.4\pm 0.3$ $3.0\pm 0.3$ 0.08 $14.2\pm 0.2$ $0.3\pm 0.0$ 2.8 $2.8\pm 0.1$ 67
99.9575 9.8404 $27.4\times 21.7$ 145.0 15.9 $1.6\pm 0.2$ $3.0\pm 0.2$ 0.06
$13.9\pm 0.2$ $0.3\pm 0.0$ 2.5 $2.4\pm 0.0$ 68 100.2132 8.9324 $43.1\times
32.7$ 148.7 20.8 $3.7\pm 0.6$ $14.0\pm 0.9$ 0.11 $13.0\pm 0.2$ $1.4\pm 0.1$
0.86 $4.3\pm 0.1$ 69 99.9438 9.82 $50.0\times 46.5$ 57.5 30.9 $2.1\pm 0.2$
$14.0\pm 0.5$ 0.16 $13.9\pm 0.2$ $1.5\pm 0.1$ 1.2 $2.3\pm 0.0$ 70 100.3765
9.2731 $54.6\times 30.9$ 52.7 28.4 $2.7\pm 0.6$ $12.0\pm 0.9$ 0.13 $13.4\pm
0.1$ $1.2\pm 0.1$ 1.2 $4.0\pm 0.2$ 71 100.3143 9.3375 $38.3\times 28.0$ 28.8
20.0 $4\pm 1$ $10\pm 2$ 0.1 $13.5\pm 0.2$ $1.1\pm 0.2$ 0.97 $7.0\pm 0.4$ 72
100.2324 9.6418 $58.2\times 39.5$ 15.8 21.7 $6\pm 2$ $28\pm 4$ 0.16 $15.1\pm
0.4$ $2.9\pm 0.4$ 0.67 $8.1\pm 0.6$ 73∗ 100.4484 8.8045 $34.9\times 28.7$ 47.8
17.4 $2.1\pm 0.3$ $5.0\pm 0.4$ 0.09 $13.6\pm 0.2$ $0.5\pm 0.0$ 1.9 $3.5\pm
0.1$ 74 100.22 8.9826 $30.1\times 28.7$ 136.3 17.6 $3\pm 1$ $8\pm 1$ 0.08
$13.3\pm 0.1$ $0.8\pm 0.1$ 1.1 $5.4\pm 0.2$ 75 100.6594 9.4211 $71.2\times
35.8$ 108.8 31.6 $3.7\pm 0.8$ $23\pm 2$ 0.16 $13.1\pm 0.2$ $2.4\pm 0.2$ 0.75
$4.2\pm 0.3$ 76 100.2049 9.0746 $57.5\times 31.1$ 105.4 19.2 $4.0\pm 0.7$
$17\pm 1$ 0.13 $12.5\pm 0.2$ $1.8\pm 0.1$ 0.77 $5.5\pm 0.1$ 77 100.2746 9.5283
$29.5\times 21.9$ 56.9 16.8 $7\pm 4$ $13\pm 4$ 0.06 $13.2\pm 0.1$ $1.3\pm 0.4$
0.52 $20.8\pm 0.3$ 78 99.9735 9.6959 $33.6\times 25.5$ 88.5 15.4 $7\pm 2$
$13\pm 2$ 0.08 $13.0\pm 0.2$ $1.3\pm 0.2$ 0.67 $7.7\pm 0.3$ 79 99.8682 9.6277
$49.2\times 29.9$ 173.2 20.6 $12\pm 4$ $43\pm 5$ 0.12 $11.9\pm 0.1$ $4.4\pm
0.5$ 0.27 $7.7\pm 0.8$ 80 100.1543 9.8742 $79.4\times 50.0$ 161.9 36.8 $9\pm
2$ $89\pm 4$ 0.21 $18.5\pm 0.7$ $9.2\pm 0.4$ 0.35 $5.8\pm 0.4$ 81 100.085
9.8963 $54.0\times 27.2$ 71.2 18.8 $2.0\pm 0.4$ $6.0\pm 0.5$ 0.12 $17.7\pm
0.5$ $0.6\pm 0.1$ 2.9 $4.2\pm 0.1$ 82 100.0651 9.4623 $43.9\times 35.3$ 62.1
19.6 $2.5\pm 0.7$ $10\pm 1$ 0.12 $13.6\pm 0.2$ $1.1\pm 0.1$ 1.3 $3.7\pm 0.1$
83∗ 99.8388 9.2951 $32.8\times 24.9$ 151.4 17.0 $1.4\pm 0.4$ $3.0\pm 0.5$ 0.08
$13.8\pm 0.2$ $0.3\pm 0.1$ 2.7 $2.9\pm 0.0$ 84 100.5825 9.0739 $50.9\times
31.0$ 162.1 19.2 $4\pm 1$ $16\pm 2$ 0.12 $12.3\pm 0.1$ $1.6\pm 0.2$ 0.79
$5.9\pm 0.1$ 85 100.0642 9.4975 $55.7\times 32.6$ 69.7 20.0 $3.0\pm 0.4$
$14.0\pm 0.7$ 0.14 $13.6\pm 0.2$ $1.4\pm 0.1$ 1.1 $3.9\pm 0.1$ 86 100.1978
9.7694 $35.2\times 35.1$ 24.4 18.8 $1.9\pm 0.7$ $6\pm 1$ 0.11 $22.0\pm 0.5$
$0.7\pm 0.1$ 3.0 $4.8\pm 0.2$ 87 99.9733 9.706 $30.7\times 26.9$ 48.4 14.7
$7\pm 3$ $14\pm 3$ 0.08 $12.9\pm 0.2$ $1.4\pm 0.3$ 0.59 $8.2\pm 0.3$ 88
100.1362 9.886 $39.5\times 26.6$ 15.5 16.8 $6\pm 2$ $14\pm 2$ 0.09 $17.7\pm
0.7$ $1.5\pm 0.2$ 0.92 $6.5\pm 0.4$ 89 100.3631 9.3662 $32.6\times 26.1$ 175.5
12.0 $11\pm 2$ $18\pm 2$ 0.08 $12.6\pm 0.1$ $1.8\pm 0.2$ 0.46 $11.6\pm 0.6$ 90
100.5747 9.0 $33.8\times 25.5$ 74.7 14.3 $3.0\pm 0.9$ $6\pm 1$ 0.08 $12.5\pm
0.1$ $0.6\pm 0.1$ 1.4 $5.4\pm 0.2$ 91 100.2115 9.5879 $38.6\times 34.9$ 27.1
13.7 $19\pm 7$ $51\pm 8$ 0.11 $12.9\pm 0.2$ $5.2\pm 0.8$ 0.23 $20\pm 1$ 92
100.1875 9.6607 $39.5\times 36.2$ 112.1 13.0 $3\pm 1$ $10\pm 2$ 0.12 $16.8\pm
0.3$ $1.0\pm 0.2$ 1.6 $5.6\pm 0.2$ 93 100.0378 9.7167 $66.5\times 48.9$ 95.0
29.8 $4\pm 1$ $30\pm 2$ 0.19 $15.0\pm 0.4$ $3.1\pm 0.2$ 0.76 $3.9\pm 0.1$ 94
100.1673 9.7977 $38.5\times 33.2$ 19.4 20.2 $1.7\pm 0.5$ $6.0\pm 0.8$ 0.11
$20.6\pm 0.2$ $0.6\pm 0.1$ 3.0 $3.4\pm 0.1$ 95 100.2196 8.9951 $64.8\times
34.5$ 150.7 19.5 $4.3\pm 0.9$ $22\pm 2$ 0.15 $13.1\pm 0.1$ $2.3\pm 0.2$ 0.72
$6.0\pm 0.2$ 96 100.6249 9.0262 $48.3\times 35.3$ 63.9 21.1 $2.9\pm 0.4$
$13.0\pm 0.6$ 0.13 $12.8\pm 0.1$ $1.3\pm 0.1$ 1.0 $4.3\pm 0.0$ 97 100.5551
8.9896 $25.9\times 24.6$ 164.2 13.3 $2\pm 1$ $3\pm 1$ 0.06 $13.3\pm 0.3$
$0.4\pm 0.1$ 1.7 $5.1\pm 0.2$ 98 100.7344 9.3178 $69.8\times 39.3$ 175.9 27.8
$3.5\pm 0.4$ $25.0\pm 0.8$ 0.17 $13.1\pm 0.1$ $2.6\pm 0.1$ 0.72 $4.0\pm 0.1$
99 100.3448 9.3903 $59.3\times 44.4$ 116.7 20.2 $8\pm 3$ $51\pm 5$ 0.17
$13.5\pm 0.1$ $5.3\pm 0.5$ 0.35 $13.6\pm 0.6$ 100 100.096 9.8506 $42.0\times
34.8$ 162.8 20.8 $3\pm 1$ $10\pm 2$ 0.12 $19.0\pm 0.4$ $1.0\pm 0.2$ 1.9
$4.4\pm 0.4$ 101 100.2797 8.9593 $39.6\times 25.9$ 17.2 14.6 $1.7\pm 0.4$
$4.0\pm 0.5$ 0.09 $13.6\pm 0.1$ $0.4\pm 0.1$ 2.5 $3.8\pm 0.1$ 102 100.1655
8.9167 $33.1\times 30.1$ 76.7 16.0 $2.8\pm 0.9$ $8\pm 1$ 0.09 $13.2\pm 0.1$
$0.8\pm 0.1$ 1.2 $5.0\pm 0.2$ 103 100.2451 9.5575 $32.0\times 25.2$ 151.1 13.8
$7\pm 5$ $12\pm 6$ 0.08 $14.2\pm 0.2$ $1.2\pm 0.6$ 0.75 $18\pm 1$ 104 100.1744
9.7514 $73.3\times 56.5$ 101.7 32.5 $2.6\pm 0.6$ $27\pm 2$ 0.22 $21.2\pm 0.8$
$2.8\pm 0.2$ 1.4 $3.6\pm 0.2$ 105 100.2541 9.3378 $65.8\times 42.6$ 179.1 19.8
$2.9\pm 0.6$ $18\pm 1$ 0.17 $13.5\pm 0.2$ $1.9\pm 0.1$ 1.0 $4.6\pm 0.1$ 106
100.2503 9.59 $37.3\times 29.1$ 125.1 13.9 $45\pm 11$ $114\pm 12$ 0.1 $14.8\pm
0.5$ $12\pm 1$ 0.1 $44.1\pm 0.6$ 107 100.0727 9.4603 $31.7\times 24.6$ 129.4
9.9 $1.9\pm 0.8$ $4.0\pm 0.9$ 0.07 $13.5\pm 0.1$ $0.4\pm 0.1$ 2.0 $4.0\pm 0.2$
108∗ 100.434 8.8942 $28.9\times 23.4$ 103.2 14.4 $1.4\pm 0.5$ $3.0\pm 0.6$
0.06 $13.5\pm 0.2$ $0.3\pm 0.1$ 2.6 $3.0\pm 0.1$ 109 100.2467 9.0244
$63.2\times 35.1$ 172.2 21.4 $4\pm 1$ $16\pm 2$ 0.15 $12.5\pm 0.2$ $1.7\pm
0.2$ 0.93 $6.7\pm 0.7$ 110 100.0598 9.213 $34.0\times 24.7$ 154.0 15.3 $1.7\pm
0.3$ $4.0\pm 0.4$ 0.08 $13.4\pm 0.1$ $0.4\pm 0.0$ 2.1 $5.0\pm 0.1$ 111
100.2915 9.9672 $29.9\times 25.9$ 42.8 14.5 $2.3\pm 0.9$ $5\pm 1$ 0.07
$13.3\pm 0.1$ $0.5\pm 0.1$ 1.6 $6.2\pm 0.1$ 112 100.094 9.9152 $23.7\times
23.7$ 149.1 13.4 $1.7\pm 0.6$ $3.0\pm 0.7$ 0.05 $16.4\pm 0.2$ $0.3\pm 0.1$ 2.5
$4.4\pm 0.0$ 113 100.1795 9.1332 $35.3\times 30.0$ 20.3 14.0 $2.7\pm 0.8$
$7\pm 1$ 0.09 $13.2\pm 0.1$ $0.8\pm 0.1$ 1.3 $6.7\pm 0.0$ 114 100.1754 9.1555
$37.9\times 29.6$ 32.7 14.2 $3\pm 1$ $8\pm 1$ 0.1 $13.0\pm 0.1$ $0.8\pm 0.1$
1.3 $6.5\pm 0.2$ 115 100.2806 9.7276 $50.4\times 35.6$ 53.6 12.1 $3\pm 2$
$10\pm 3$ 0.13 $14.6\pm 0.1$ $1.0\pm 0.3$ 1.6 $8.8\pm 0.4$ 116 100.1912 9.6705
$67.9\times 49.3$ 133.8 24.3 $3\pm 1$ $29\pm 3$ 0.19 $17.2\pm 0.5$ $3.1\pm
0.3$ 0.88 $4.8\pm 0.3$ 117 99.9587 9.9553 $37.3\times 28.3$ 138.8 13.7 $2.9\pm
0.9$ $7\pm 1$ 0.09 $15.7\pm 0.2$ $0.8\pm 0.1$ 1.5 $4.0\pm 0.2$ 118 100.4639
9.2354 $54.6\times 41.8$ 155.9 21.4 $1.9\pm 0.9$ $11\pm 2$ 0.16 $13.5\pm 0.1$
$1.1\pm 0.2$ 1.6 $4.8\pm 0.1$ 119 100.7421 9.4783 $44.0\times 31.9$ 47.3 18.3
$1.2\pm 0.1$ $5.0\pm 0.2$ 0.11 $14.3\pm 0.2$ $0.5\pm 0.0$ 2.7 $2.3\pm 0.0$ 120
100.4643 9.4295 $46.0\times 42.1$ 11.5 24.5 $2.5\pm 0.6$ $14\pm 1$ 0.14
$13.7\pm 0.2$ $1.4\pm 0.1$ 1.1 $4.2\pm 0.2$ 121 100.1103 9.5779 $40.7\times
31.8$ 0.8 13.9 $2.6\pm 0.8$ $8\pm 1$ 0.11 $15.0\pm 0.1$ $0.9\pm 0.1$ 1.5
$7.2\pm 0.1$ 122 100.1735 9.8958 $30.9\times 23.3$ 93.5 13.3 $1.1\pm 0.5$
$2.0\pm 0.5$ 0.07 $17.7\pm 0.2$ $0.2\pm 0.1$ 5.3 $3.8\pm 0.1$ 123 100.3207
9.7433 $49.9\times 42.8$ 35.8 20.8 $4\pm 2$ $20\pm 3$ 0.15 $14.5\pm 0.1$
$2.1\pm 0.3$ 0.85 $6.9\pm 0.5$ 124 100.642 9.5663 $33.6\times 27.6$ 56.7 14.1
$1.5\pm 0.4$ $4.0\pm 0.5$ 0.09 $13.8\pm 0.1$ $0.4\pm 0.1$ 2.5 $3.2\pm 0.1$ 125
100.2854 9.5877 $33.2\times 27.5$ 52.6 12.5 $23\pm 13$ $49\pm 14$ 0.08
$13.4\pm 0.3$ $5\pm 1$ 0.18 $51\pm 2$ 126 100.1556 9.8406 $44.1\times 34.0$
15.8 12.6 $2\pm 1$ $7\pm 2$ 0.12 $18.5\pm 0.4$ $0.7\pm 0.2$ 2.6 $4.2\pm 0.3$
127 100.4369 8.8042 $39.6\times 28.4$ 19.0 12.9 $1.8\pm 0.2$ $4.0\pm 0.3$ 0.1
$13.6\pm 0.2$ $0.4\pm 0.0$ 2.5 $3.4\pm 0.0$ 128∗ 100.3264 9.3515 $37.5\times
28.1$ 42.0 12.1 $5\pm 2$ $11\pm 2$ 0.09 $13.5\pm 0.2$ $1.2\pm 0.2$ 0.88
$9.8\pm 0.5$ 129 100.1184 9.8625 $59.1\times 37.7$ 96.4 13.4 $4\pm 1$ $21\pm
2$ 0.15 $19.7\pm 0.5$ $2.1\pm 0.2$ 1.2 $5.6\pm 0.3$

Table A1: Catalog of clumps (continued).

# Alp Del $A\times B$ PA Sig $N^{\rm peak}_{\rm H_{2}}$ $N^{\rm int}_{\rm
H_{2}}$ FWHMdecbbfootnotemark: $b$ $T_{\rm dust}$ $M$ $\alpha_{\rm BE}$
$N^{\rm back}_{\rm H_{2}}$ [J2000] [J2000] [$"\times"$] [$\degree$] [Av
units]aafootnotemark: $a$ [Av units]aafootnotemark: $a$ [pc] [K] [$M_{\odot}$]
[Av units]aafootnotemark: $a$ 130 100.4482 8.792 $45.3\times 30.7$ 173.2 14.2
$1.7\pm 0.3$ $6.0\pm 0.4$ 0.11 $13.6\pm 0.2$ $0.6\pm 0.1$ 2.3 $3.1\pm 0.1$
131∗ 100.1699 8.9387 $43.1\times 25.1$ 53.0 14.0 $2.7\pm 0.5$ $8.0\pm 0.8$ 0.1
$13.1\pm 0.1$ $0.8\pm 0.1$ 1.3 $6.3\pm 0.2$ 132 100.583 9.415 $29.9\times
24.0$ 73.2 11.9 $1.7\pm 0.8$ $3.0\pm 0.9$ 0.07 $13.4\pm 0.1$ $0.3\pm 0.1$ 2.5
$4.9\pm 0.2$ 133 100.0878 9.2379 $70.8\times 41.2$ 29.5 20.6 $2.2\pm 0.5$
$16\pm 1$ 0.18 $13.6\pm 0.1$ $1.7\pm 0.1$ 1.2 $4.5\pm 0.0$ 134 100.2048 9.4749
$34.9\times 24.6$ 19.8 12.4 $1.7\pm 0.6$ $4.0\pm 0.7$ 0.08 $14.9\pm 0.1$
$0.4\pm 0.1$ 2.6 $5.6\pm 0.1$ 135 100.3867 9.7979 $40.7\times 38.9$ 67.4 15.3
$1.5\pm 0.3$ $6.0\pm 0.4$ 0.12 $15.1\pm 0.2$ $0.6\pm 0.0$ 2.5 $2.9\pm 0.1$ 136
100.436 9.2739 $42.4\times 25.8$ 79.5 13.3 $1.6\pm 0.4$ $5.0\pm 0.6$ 0.1
$14.0\pm 0.1$ $0.5\pm 0.1$ 2.3 $4.0\pm 0.1$ 137 100.2108 9.6019 $28.3\times
25.4$ 72.3 12.2 $8\pm 3$ $16\pm 3$ 0.07 $13.5\pm 0.2$ $1.7\pm 0.3$ 0.45
$15.7\pm 0.5$ 138 100.09 9.5957 $33.9\times 24.3$ 174.1 13.1 $1.7\pm 0.6$
$4.0\pm 0.7$ 0.08 $15.8\pm 0.1$ $0.4\pm 0.1$ 2.7 $5.5\pm 0.1$ 139 100.0368
9.6407 $24.3\times 24.2$ 47.6 11.4 $2.4\pm 0.6$ $3.0\pm 0.6$ 0.06 $16.1\pm
0.2$ $0.4\pm 0.1$ 2.1 $5.9\pm 0.0$ 140 99.9155 9.5519 $53.3\times 34.0$ 22.3
14.3 $6\pm 2$ $24\pm 2$ 0.13 $12.8\pm 0.2$ $2.5\pm 0.2$ 0.57 $4.0\pm 0.4$ 141
100.1577 9.0647 $64.2\times 36.4$ 154.7 15.8 $3\pm 1$ $14\pm 2$ 0.16 $12.6\pm
0.2$ $1.5\pm 0.2$ 1.1 $5.4\pm 0.2$ 142 100.0998 9.1946 $46.0\times 34.1$ 103.0
16.3 $1.7\pm 0.6$ $8\pm 1$ 0.12 $13.6\pm 0.1$ $0.8\pm 0.1$ 1.7 $4.1\pm 0.1$
143 100.2731 9.6803 $39.9\times 29.9$ 127.7 12.9 $3\pm 1$ $9\pm 2$ 0.1
$14.5\pm 0.2$ $0.9\pm 0.2$ 1.4 $7.3\pm 0.4$ 144 100.4178 9.8553 $61.1\times
44.9$ 46.0 18.3 $1.6\pm 0.2$ $11.0\pm 0.4$ 0.17 $13.8\pm 0.2$ $1.2\pm 0.0$ 1.7
$2.7\pm 0.0$ 145 100.2594 9.8615 $38.6\times 32.2$ 159.2 13.2 $1.6\pm 0.8$
$5\pm 1$ 0.11 $15.9\pm 0.2$ $0.5\pm 0.1$ 2.8 $6.1\pm 0.3$ 146 100.0022 9.1453
$81.3\times 49.2$ 8.8 20.2 $2.6\pm 0.7$ $25\pm 2$ 0.21 $13.8\pm 0.1$ $2.5\pm
0.2$ 0.97 $4.0\pm 0.2$ 147 100.3188 9.3958 $45.5\times 31.7$ 171.5 11.9 $9\pm
2$ $28\pm 3$ 0.12 $14.2\pm 0.2$ $2.9\pm 0.3$ 0.0 $16.9\pm 0.6$ 148 100.2951
9.5625 $29.1\times 23.3$ 2.6 11.4 $13\pm 6$ $22\pm 7$ 0.07 $12.6\pm 0.2$
$2.2\pm 0.7$ 0.31 $27.4\pm 0.3$ 149 100.1934 9.6148 $37.1\times 29.3$ 167.1
11.4 $5\pm 2$ $13\pm 2$ 0.1 $14.5\pm 0.2$ $1.4\pm 0.2$ 0.83 $10.6\pm 0.3$ 150
100.2077 9.7861 $46.5\times 34.8$ 43.1 14.7 $1.7\pm 0.7$ $7\pm 1$ 0.13
$21.6\pm 0.5$ $0.8\pm 0.1$ 2.8 $5.1\pm 0.1$ 151 100.0163 9.9338 $31.8\times
30.7$ 77.5 13.0 $1.3\pm 0.3$ $4.0\pm 0.4$ 0.09 $16.0\pm 0.1$ $0.4\pm 0.1$ 3.1
$3.5\pm 0.1$ 152 100.6882 9.3968 $39.1\times 34.3$ 31.1 15.7 $1.4\pm 0.6$
$5\pm 1$ 0.11 $13.4\pm 0.1$ $0.5\pm 0.1$ 2.6 $2.9\pm 0.1$ 153 100.5352 9.6892
$24.5\times 23.1$ 25.0 11.5 $0.6\pm 0.2$ $1.0\pm 0.2$ 0.05 $15.0\pm 0.1$
$0.1\pm 0.0$ 6.1 $2.5\pm 0.0$ 154 99.7937 9.7389 $31.5\times 26.2$ 73.7 11.6
$1.2\pm 0.1$ $2.0\pm 0.1$ 0.08 $14.3\pm 0.1$ $0.2\pm 0.0$ 5.4 $2.3\pm 0.1$ 155
100.5735 8.9423 $35.2\times 30.5$ 162.6 10.3 $3.1\pm 0.7$ $9\pm 1$ 0.1
$12.7\pm 0.1$ $1.0\pm 0.1$ 1.0 $4.8\pm 0.2$ 156 100.1781 8.9031 $31.2\times
29.2$ 138.6 12.5 $1.7\pm 0.7$ $4\pm 1$ 0.08 $13.5\pm 0.1$ $0.4\pm 0.1$ 2.4
$3.9\pm 0.2$ 157 100.398 9.5311 $33.2\times 24.1$ 30.6 10.8 $1.6\pm 0.4$
$3.0\pm 0.4$ 0.08 $13.9\pm 0.1$ $0.3\pm 0.0$ 2.7 $4.1\pm 0.0$ 158 100.1566
9.7885 $50.2\times 32.7$ 149.7 13.0 $1.4\pm 0.4$ $5.0\pm 0.5$ 0.13 $20.2\pm
0.2$ $0.6\pm 0.1$ 3.9 $3.8\pm 0.1$ 159 100.3769 8.8673 $56.4\times 33.4$ 92.8
13.2 $2.0\pm 0.4$ $8.0\pm 0.6$ 0.14 $13.7\pm 0.1$ $0.8\pm 0.1$ 1.9 $3.7\pm
0.0$ 160∗ 100.5798 9.03 $36.2\times 26.0$ 14.2 12.1 $1.9\pm 0.7$ $5.0\pm 0.9$
0.09 $12.9\pm 0.2$ $0.5\pm 0.1$ 1.8 $4.3\pm 0.1$ 161 100.467 9.1946
$35.8\times 23.0$ 66.2 12.4 $1.1\pm 0.6$ $3.0\pm 0.8$ 0.08 $13.9\pm 0.1$
$0.3\pm 0.1$ 3.4 $3.6\pm 0.1$ 162 100.5067 9.4543 $39.9\times 32.4$ 59.8 13.2
$1.1\pm 0.2$ $4.0\pm 0.3$ 0.11 $13.9\pm 0.1$ $0.4\pm 0.0$ 3.0 $2.5\pm 0.0$ 163
100.2866 9.4104 $26.2\times 23.0$ 27.2 11.1 $4\pm 2$ $7\pm 2$ 0.06 $16.4\pm
0.3$ $0.7\pm 0.2$ 1.1 $9.8\pm 0.3$ 164 100.2973 9.5064 $34.1\times 25.5$ 17.6
10.8 $28\pm 7$ $50\pm 7$ 0.08 $14.1\pm 0.7$ $5.2\pm 0.8$ 0.18 $44\pm 3$ 165
100.1823 9.0229 $68.1\times 57.3$ 99.1 14.6 $2\pm 1$ $23\pm 3$ 0.21 $13.1\pm
0.1$ $2.4\pm 0.3$ 0.95 $5.6\pm 0.5$ 166 100.1345 9.127 $71.8\times 60.0$ 3.2
20.6 $3.4\pm 0.9$ $39\pm 2$ 0.22 $13.3\pm 0.1$ $4.0\pm 0.3$ 0.61 $5.0\pm 0.2$
167 100.3678 9.5892 $27.1\times 23.7$ 50.0 10.1 $3.2\pm 0.6$ $5.0\pm 0.6$ 0.06
$14.6\pm 0.2$ $0.5\pm 0.1$ 1.4 $5.6\pm 0.0$ 168 100.1722 9.9264 $42.3\times
34.0$ 106.6 12.4 $1.1\pm 0.4$ $4.0\pm 0.7$ 0.12 $16.7\pm 0.1$ $0.4\pm 0.1$ 3.7
$3.4\pm 0.1$ 169 100.3882 9.325 $36.5\times 31.0$ 31.4 10.8 $2\pm 1$ $6\pm 1$
0.1 $13.4\pm 0.1$ $0.6\pm 0.1$ 1.8 $7.1\pm 0.1$ 170 100.2085 9.6972
$35.8\times 24.6$ 49.7 10.8 $1.2\pm 0.5$ $3.0\pm 0.6$ 0.08 $17.5\pm 0.2$
$0.3\pm 0.1$ 4.4 $3.9\pm 0.1$ 171 100.1884 9.8296 $41.4\times 32.7$ 64.5 15.5
$0.8\pm 0.2$ $3.0\pm 0.3$ 0.11 $18.8\pm 0.4$ $0.3\pm 0.0$ 5.6 $2.8\pm 0.0$ 172
99.8817 9.6786 $27.6\times 24.2$ 165.0 10.9 $1.2\pm 0.3$ $2.0\pm 0.4$ 0.06
$13.8\pm 0.1$ $0.2\pm 0.0$ 3.3 $4.0\pm 0.0$ 173 100.5193 8.8943 $32.0\times
24.2$ 139.5 10.9 $1.3\pm 0.3$ $3.0\pm 0.4$ 0.07 $13.5\pm 0.2$ $0.3\pm 0.0$ 2.9
$2.8\pm 0.0$ 174 100.111 8.9833 $57.2\times 32.3$ 44.0 13.6 $2.1\pm 0.8$ $9\pm
1$ 0.14 $13.3\pm 0.1$ $0.9\pm 0.1$ 1.7 $4.1\pm 0.2$ 175 100.4885 9.5556
$74.0\times 45.2$ 52.5 13.8 $1.1\pm 0.2$ $9.0\pm 0.5$ 0.19 $14.0\pm 0.2$
$0.9\pm 0.1$ 2.4 $2.6\pm 0.1$ 176 99.9884 9.3223 $34.1\times 31.4$ 143.5 10.7
$1.4\pm 0.2$ $4.0\pm 0.3$ 0.1 $13.4\pm 0.1$ $0.4\pm 0.0$ 2.6 $3.6\pm 0.0$ 177
100.2578 9.5549 $33.4\times 27.7$ 82.4 10.1 $14\pm 9$ $30\pm 9$ 0.09 $12.7\pm
0.1$ $3.1\pm 0.9$ 0.29 $33\pm 4$ 178 99.9995 9.3985 $42.9\times 41.9$ 123.5
15.6 $1.3\pm 0.2$ $7.0\pm 0.3$ 0.13 $13.8\pm 0.1$ $0.7\pm 0.0$ 2.2 $2.5\pm
0.0$ 179 100.2595 9.7602 $55.4\times 33.0$ 160.7 12.0 $1.6\pm 0.6$ $6\pm 1$
0.14 $15.4\pm 0.1$ $0.7\pm 0.1$ 2.6 $7.0\pm 0.1$ 180 100.2866 9.0061
$40.1\times 30.9$ 24.0 11.7 $1.9\pm 0.8$ $6\pm 1$ 0.11 $13.5\pm 0.1$ $0.6\pm
0.1$ 2.0 $4.6\pm 0.2$ 181 100.113 9.1433 $56.7\times 49.6$ 12.3 13.7 $1.8\pm
0.6$ $12\pm 1$ 0.17 $13.6\pm 0.1$ $1.3\pm 0.1$ 1.5 $4.7\pm 0.1$ 182 100.4264
9.4137 $72.1\times 34.7$ 16.4 13.3 $1.0\pm 0.4$ $5.0\pm 0.7$ 0.16 $15.1\pm
0.1$ $0.6\pm 0.1$ 3.7 $4.1\pm 0.1$ 183 100.1023 9.5246 $47.2\times 35.8$ 48.0
12.3 $1.2\pm 0.4$ $5.0\pm 0.6$ 0.13 $14.4\pm 0.2$ $0.5\pm 0.1$ 3.3 $4.0\pm
0.1$ 184 100.2541 9.656 $34.5\times 30.1$ 30.6 14.3 $2\pm 1$ $6\pm 1$ 0.09
$15.5\pm 0.1$ $0.6\pm 0.1$ 2.0 $7.1\pm 0.2$ 185 100.5251 9.1592 $53.8\times
41.0$ 54.7 12.1 $7\pm 2$ $37\pm 3$ 0.15 $12.3\pm 0.3$ $3.8\pm 0.3$ 0.41
$10.0\pm 0.7$ 186 100.7216 9.2688 $48.9\times 39.1$ 123.7 13.3 $2.1\pm 0.6$
$10\pm 1$ 0.14 $13.5\pm 0.1$ $1.1\pm 0.1$ 1.4 $3.6\pm 0.1$ 187 100.6924 9.3673
$37.0\times 33.5$ 51.6 12.1 $1.4\pm 0.5$ $5.0\pm 0.8$ 0.11 $13.4\pm 0.1$
$0.5\pm 0.1$ 2.5 $4.0\pm 0.0$ 188 100.4766 9.4058 $44.4\times 33.3$ 122.5 11.4
$1.3\pm 0.5$ $5.0\pm 0.8$ 0.12 $14.6\pm 0.1$ $0.5\pm 0.1$ 2.9 $3.6\pm 0.1$ 189
100.0175 9.6433 $35.3\times 33.8$ 84.8 10.7 $1.4\pm 0.5$ $4.0\pm 0.7$ 0.1
$15.8\pm 0.1$ $0.4\pm 0.1$ 3.5 $5.2\pm 0.2$ 190 100.4665 8.9984 $31.6\times
23.2$ 128.7 10.6 $1.0\pm 0.2$ $2.0\pm 0.3$ 0.07 $13.7\pm 0.1$ $0.2\pm 0.0$ 3.5
$3.0\pm 0.0$ 191 100.2866 8.9851 $42.1\times 30.4$ 51.5 11.3 $1.8\pm 0.6$
$6.0\pm 0.8$ 0.11 $13.8\pm 0.1$ $0.6\pm 0.1$ 2.1 $4.0\pm 0.1$ 192 100.332
9.3187 $53.7\times 29.8$ 85.9 12.4 $1.8\pm 0.9$ $6\pm 1$ 0.12 $14.4\pm 0.1$
$0.7\pm 0.1$ 2.1 $5.3\pm 0.2$ 193 100.0675 9.1705 $28.1\times 22.7$ 166.9 9.8
$1.4\pm 0.4$ $2.0\pm 0.5$ 0.06 $13.4\pm 0.1$ $0.2\pm 0.1$ 3.1 $5.1\pm 0.0$ 194
100.307 9.4094 $56.4\times 31.8$ 26.6 9.7 $23\pm 3$ $83\pm 5$ 0.13 $13.8\pm
0.3$ $8.6\pm 0.5$ 0.18 $18\pm 1$ 195 100.1469 9.5938 $43.4\times 40.0$ 40.8
12.9 $3\pm 1$ $14\pm 3$ 0.13 $16.7\pm 0.3$ $1.5\pm 0.3$ 1.2 $7.1\pm 0.3$

Table A1: Catalog of clumps (continued).

# Alp Del $A\times B$ PA Sig $N^{\rm peak}_{\rm H_{2}}$ $N^{\rm int}_{\rm
H_{2}}$ FWHMdecbbfootnotemark: $b$ $T_{\rm dust}$ $M$ $\alpha_{\rm BE}$
$N^{\rm back}_{\rm H_{2}}$ [J2000] [J2000] [$"\times"$] [$\degree$] [Av
units]aafootnotemark: $a$ [Av units]aafootnotemark: $a$ [pc] [K] [$M_{\odot}$]
[Av units]aafootnotemark: $a$ 196 100.3293 9.8023 $47.4\times 28.1$ 140.5 11.3
$1\pm 1$ $3\pm 1$ 0.11 $14.8\pm 0.2$ $0.3\pm 0.1$ 4.5 $5.4\pm 0.4$ 197
100.0306 9.9795 $46.5\times 38.3$ 161.4 11.9 $2.3\pm 0.4$ $10.0\pm 0.6$ 0.13
$15.8\pm 0.2$ $1.0\pm 0.1$ 1.7 $2.9\pm 0.1$ 198 100.5287 9.2781 $52.4\times
39.1$ 18.0 14.0 $2.0\pm 0.6$ $10\pm 1$ 0.15 $13.6\pm 0.1$ $1.1\pm 0.1$ 1.5
$4.0\pm 0.1$ 199 100.4731 9.3384 $37.7\times 25.7$ 38.9 9.9 $1.7\pm 0.3$
$4.0\pm 0.3$ 0.09 $14.3\pm 0.1$ $0.4\pm 0.0$ 2.7 $4.6\pm 0.2$ 200 100.4175
9.4934 $39.8\times 27.9$ 9.9 10.2 $1.7\pm 0.5$ $4.0\pm 0.6$ 0.1 $14.2\pm 0.2$
$0.4\pm 0.1$ 2.6 $4.1\pm 0.0$ 201 100.5678 9.6361 $37.4\times 23.9$ 15.1 10.0
$0.7\pm 0.2$ $1.0\pm 0.3$ 0.08 $14.3\pm 0.1$ $0.1\pm 0.0$ 7.0 $2.7\pm 0.0$ 202
100.1883 9.5249 $48.1\times 29.7$ 173.9 10.2 $2.9\pm 0.7$ $9.0\pm 0.9$ 0.12
$15.0\pm 0.1$ $0.9\pm 0.1$ 1.5 $6.5\pm 0.1$ 203 100.2228 9.6119 $43.5\times
28.1$ 7.9 9.7 $20\pm 6$ $57\pm 7$ 0.1 $12.9\pm 0.2$ $5.9\pm 0.7$ 0.19 $19\pm
2$ 204 100.6019 8.9422 $34.6\times 31.9$ 12.9 11.5 $1.9\pm 0.6$ $6.0\pm 0.9$
0.1 $13.1\pm 0.1$ $0.6\pm 0.1$ 1.8 $4.5\pm 0.1$ 205 100.3301 9.6818
$56.2\times 34.8$ 43.8 12.3 $4\pm 2$ $17\pm 3$ 0.14 $14.5\pm 0.1$ $1.7\pm 0.3$
1.0 $7.7\pm 0.6$ 206 100.1509 9.8352 $61.0\times 36.1$ 178.2 11.7 $1\pm 1$
$6\pm 2$ 0.15 $18.3\pm 0.4$ $0.7\pm 0.2$ 3.3 $4.3\pm 0.3$ 207 100.0777 9.8355
$33.5\times 22.9$ 171.4 10.2 $1.1\pm 0.7$ $2.0\pm 0.8$ 0.07 $18.4\pm 0.2$
$0.2\pm 0.1$ 5.3 $4.2\pm 0.2$ 208 100.393 8.7956 $27.3\times 22.9$ 61.5 9.9
$0.8\pm 0.2$ $1.0\pm 0.3$ 0.06 $13.9\pm 0.1$ $0.1\pm 0.0$ 4.9 $3.0\pm 0.0$ 209
100.6358 9.2201 $41.5\times 28.3$ 121.7 10.3 $1.1\pm 0.4$ $3.0\pm 0.5$ 0.1
$13.8\pm 0.1$ $0.3\pm 0.1$ 3.9 $4.3\pm 0.1$ 210∗ 100.1728 9.0687 $52.5\times
36.9$ 37.7 10.6 $1.8\pm 0.8$ $7\pm 1$ 0.14 $12.7\pm 0.1$ $0.7\pm 0.1$ 2.1
$6.8\pm 0.2$ 211 99.8251 9.6745 $39.1\times 26.2$ 141.1 11.2 $0.8\pm 0.3$
$2.0\pm 0.5$ 0.09 $14.3\pm 0.1$ $0.2\pm 0.1$ 4.8 $2.5\pm 0.1$ 212 100.4328
9.4874 $56.5\times 46.5$ 116.7 13.3 $2.1\pm 0.4$ $13.0\pm 0.7$ 0.17 $14.2\pm
0.1$ $1.3\pm 0.1$ 1.5 $4.0\pm 0.1$ 213 100.1546 9.1013 $50.2\times 30.6$ 122.3
10.7 $1.8\pm 0.8$ $6\pm 1$ 0.12 $12.8\pm 0.1$ $0.6\pm 0.1$ 2.2 $6.7\pm 0.4$
214 99.8508 9.513 $49.7\times 39.1$ 4.2 18.1 $0.8\pm 0.2$ $4.0\pm 0.3$ 0.14
$14.1\pm 0.1$ $0.4\pm 0.0$ 3.8 $1.7\pm 0.0$ 215 100.2164 9.9611 $51.6\times
29.7$ 47.7 11.0 $1.3\pm 0.5$ $5.0\pm 0.8$ 0.12 $16.5\pm 0.1$ $0.5\pm 0.1$ 3.5
$3.8\pm 0.1$ 216 100.1868 9.5419 $36.1\times 31.9$ 29.6 10.9 $2\pm 1$ $7\pm 2$
0.1 $15.1\pm 0.1$ $0.7\pm 0.2$ 1.8 $8.2\pm 0.5$ 217 100.393 9.7742 $67.7\times
65.9$ 157.0 19.8 $1.9\pm 0.4$ $21.0\pm 0.9$ 0.22 $14.5\pm 0.2$ $2.1\pm 0.1$
1.3 $3.2\pm 0.1$ 218 100.3331 9.4513 $38.3\times 32.5$ 39.8 10.5 $5\pm 2$
$15\pm 3$ 0.11 $14.2\pm 0.2$ $1.5\pm 0.3$ 0.83 $14.1\pm 0.6$ 219 100.0169
9.3053 $35.0\times 26.9$ 26.4 9.8 $1.2\pm 0.3$ $3.0\pm 0.4$ 0.09 $13.4\pm 0.1$
$0.3\pm 0.0$ 3.0 $3.8\pm 0.1$ 220 100.0327 9.9133 $34.0\times 27.6$ 144.4 9.8
$1.1\pm 0.3$ $3.0\pm 0.4$ 0.09 $16.9\pm 0.2$ $0.3\pm 0.0$ 4.7 $3.3\pm 0.1$ 221
100.2234 9.4137 $35.1\times 29.0$ 101.4 9.7 $0.8\pm 0.4$ $2.0\pm 0.6$ 0.09
$14.8\pm 0.1$ $0.3\pm 0.1$ 4.5 $3.7\pm 0.1$ 222 100.1727 9.2276 $39.8\times
33.6$ 23.4 10.6 $1.1\pm 0.3$ $4.0\pm 0.5$ 0.11 $13.9\pm 0.1$ $0.4\pm 0.1$ 3.1
$4.1\pm 0.0$ 223 100.0034 9.3185 $48.9\times 39.3$ 142.6 10.5 $1.8\pm 0.4$
$8.0\pm 0.6$ 0.14 $13.4\pm 0.1$ $0.8\pm 0.1$ 1.9 $3.5\pm 0.1$ 224 99.9801
9.3956 $41.0\times 32.4$ 177.0 11.2 $1.1\pm 0.2$ $4.0\pm 0.4$ 0.11 $13.7\pm
0.1$ $0.4\pm 0.0$ 3.1 $2.6\pm 0.0$ 225 100.168 8.8929 $38.3\times 26.7$ 23.5
9.4 $2.9\pm 0.7$ $7.0\pm 0.8$ 0.09 $13.3\pm 0.1$ $0.7\pm 0.1$ 1.5 $4.3\pm 0.1$
226 100.1345 9.7806 $38.2\times 29.7$ 13.0 9.5 $0.9\pm 0.6$ $2.0\pm 0.7$ 0.1
$20.0\pm 0.1$ $0.2\pm 0.1$ 7.5 $3.6\pm 0.1$ 227 100.1679 8.9551 $35.4\times
26.7$ 22.8 9.5 $1.9\pm 0.6$ $5.0\pm 0.8$ 0.09 $13.1\pm 0.1$ $0.5\pm 0.1$ 1.9
$6.0\pm 0.1$ 228 100.2744 9.4879 $52.5\times 34.8$ 29.2 9.8 $32\pm 10$ $130\pm
15$ 0.14 $14.4\pm 0.6$ $13\pm 2$ 0.12 $28\pm 4$ 229 100.4712 9.7146
$44.5\times 31.1$ 101.7 10.8 $0.8\pm 0.3$ $3.0\pm 0.5$ 0.11 $14.4\pm 0.1$
$0.3\pm 0.1$ 4.5 $2.8\pm 0.1$ 230 100.5055 9.0346 $40.9\times 24.4$ 13.9 9.4
$1.3\pm 0.2$ $3.0\pm 0.3$ 0.09 $13.6\pm 0.1$ $0.3\pm 0.0$ 3.0 $3.5\pm 0.1$ 231
100.6787 9.2856 $50.0\times 33.1$ 9.4 10.6 $1.5\pm 0.4$ $6.0\pm 0.6$ 0.13
$13.7\pm 0.1$ $0.6\pm 0.1$ 2.4 $3.6\pm 0.1$ 232 100.2841 9.8799 $54.2\times
47.1$ 104.2 13.7 $1.7\pm 0.8$ $11\pm 2$ 0.17 $15.0\pm 0.1$ $1.1\pm 0.2$ 1.9
$6.6\pm 0.2$ 233 100.0403 9.8045 $48.1\times 35.8$ 45.5 11.7 $0.6\pm 0.3$
$3.0\pm 0.5$ 0.13 $16.9\pm 0.3$ $0.3\pm 0.1$ 6.8 $2.7\pm 0.1$ 234 99.8508
9.4494 $22.7\times 20.4$ 94.0 10.1 $0.5\pm 0.1$ $1.0\pm 0.1$ 0.04 $14.0\pm
0.1$ $0.1\pm 0.0$ 6.1 $1.8\pm 0.0$ 235 100.5251 9.1855 $39.1\times 30.7$ 109.6
9.5 $3\pm 1$ $9\pm 2$ 0.1 $12.4\pm 0.1$ $0.9\pm 0.2$ 1.2 $12.0\pm 0.3$ 236
100.6078 9.7215 $59.5\times 39.0$ 90.2 11.8 $1.1\pm 0.2$ $6.0\pm 0.3$ 0.16
$15.6\pm 0.3$ $0.6\pm 0.0$ 3.2 $2.1\pm 0.0$ 237 100.3463 9.6186 $41.9\times
31.3$ 134.1 10.1 $0.9\pm 0.5$ $3.0\pm 0.8$ 0.11 $15.2\pm 0.1$ $0.3\pm 0.1$ 4.8
$4.6\pm 0.2$ 238 99.914 9.7416 $50.2\times 30.3$ 3.4 11.8 $0.8\pm 0.3$ $4.0\pm
0.6$ 0.12 $14.6\pm 0.1$ $0.4\pm 0.1$ 4.0 $3.1\pm 0.1$ 239 100.4243 8.8565
$53.8\times 41.1$ 16.7 10.8 $1.3\pm 0.3$ $7.0\pm 0.6$ 0.15 $13.6\pm 0.1$
$0.8\pm 0.1$ 2.3 $3.5\pm 0.1$ 240 100.1624 10.0982 $43.0\times 31.3$ 169.2 9.9
$0.9\pm 0.2$ $3.0\pm 0.3$ 0.11 $14.4\pm 0.2$ $0.3\pm 0.0$ 4.4 $2.5\pm 0.1$ 241
99.9246 9.6363 $36.2\times 28.7$ 71.0 9.2 $0.9\pm 0.3$ $2.0\pm 0.4$ 0.09
$14.5\pm 0.1$ $0.3\pm 0.1$ 4.5 $3.7\pm 0.0$ 242 100.3002 9.5817 $40.1\times
33.1$ 170.4 9.5 $16\pm 8$ $49\pm 10$ 0.11 $12.7\pm 0.3$ $5\pm 1$ 0.23 $33\pm
3$ 243∗ 100.0288 9.5179 $34.4\times 23.7$ 45.1 9.4 $0.9\pm 0.4$ $2.0\pm 0.5$
0.08 $14.1\pm 0.1$ $0.2\pm 0.1$ 4.3 $3.6\pm 0.1$ 244 100.2381 9.8821
$45.9\times 40.7$ 41.0 9.7 $1.3\pm 0.8$ $6\pm 1$ 0.14 $16.9\pm 0.4$ $0.6\pm
0.1$ 3.2 $5.1\pm 0.2$ 245 100.1924 10.1409 $50.3\times 31.8$ 90.7 9.7 $1.1\pm
0.2$ $4.0\pm 0.3$ 0.13 $14.6\pm 0.2$ $0.4\pm 0.0$ 3.9 $2.7\pm 0.1$ 246
100.5613 9.1736 $33.2\times 31.1$ 69.7 9.3 $1.7\pm 0.9$ $5\pm 1$ 0.09 $13.5\pm
0.1$ $0.5\pm 0.1$ 2.1 $5.2\pm 0.3$ 247 100.6046 9.3906 $33.4\times 25.8$ 10.3
9.3 $1.0\pm 0.5$ $2.0\pm 0.6$ 0.08 $13.9\pm 0.1$ $0.2\pm 0.1$ 3.9 $4.7\pm 0.0$
248 100.3279 9.1959 $37.9\times 28.7$ 179.6 9.2 $0.6\pm 0.1$ $2.0\pm 0.2$ 0.1
$14.4\pm 0.1$ $0.2\pm 0.0$ 6.4 $2.8\pm 0.0$ 249 100.2868 9.733 $60.7\times
49.7$ 170.2 11.1 $3\pm 1$ $24\pm 3$ 0.18 $14.5\pm 0.1$ $2.5\pm 0.4$ 0.87
$9.1\pm 0.5$ 250 100.1913 9.8507 $37.9\times 35.2$ 160.4 9.2 $0.6\pm 0.2$
$2.0\pm 0.3$ 0.11 $18.8\pm 0.3$ $0.2\pm 0.0$ 8.2 $2.8\pm 0.0$ 251 100.105
10.0504 $45.9\times 34.5$ 129.2 9.1 $0.7\pm 0.2$ $3.0\pm 0.3$ 0.12 $15.1\pm
0.1$ $0.3\pm 0.0$ 6.0 $2.1\pm 0.0$ 252 100.0991 9.6237 $46.2\times 36.4$ 22.1
9.2 $1.1\pm 0.6$ $5\pm 1$ 0.13 $16.0\pm 0.1$ $0.5\pm 0.1$ 3.4 $5.6\pm 0.1$ 253