# On the Scarcity of Dense Cores ($n>10^{5}$ cm-3) in High Latitude Planck
Galactic Cold Clumps

Fengwei Xu Kavli Institute for Astronomy and Astrophysics, Peking University,
Beijing 100871, People’s Republic of China  Department of Astronomy, School of
Physics, Peking University, Beijing, 100871, People’s Republic of China  Ke
Wang Kavli Institute for Astronomy and Astrophysics, Peking University,
Beijing 100871, People’s Republic of China  Tie Liu Shanghai Astronomical
Observatory, Chinese Academy of Sciences, 80 Nandan Road, Shanghai 200030,
People’s Republic of China  David Eden Armagh Observatory and Planetarium,
College Hill, Armagh, BT61 9DG, UK  Xunchuan Liu Shanghai Astronomical
Observatory, Chinese Academy of Sciences, 80 Nandan Road, Shanghai 200030,
People’s Republic of China  Mika Juvela Department of Physics, University of
Helsinki, PO Box 64, FI-00014 Helsinki, Finland  Jinhua He Yunnan
Observatories, Chinese Academy of Sciences, 396 Yangfangwang, Guandu District,
Kunming, 650216, People’s Republic of China  Chinese Academy of Sciences South
America Center for Astronomy,National Astronomical Observatories, CAS, Beijing
100101, People’s Republic of China  Departamento de Astronomía, Universidad de
Chile, Las Condes, 7591245 Santiago, Chile  Doug Johnstone NRC Herzberg
Astronomy and Astrophysics, 5071 West Saanich Road, Victoria, BC, V9E 2E7,
Canada  Department of Physics and Astronomy, University of Victoria, 3800
Finnerty Road, Elliot Building, Victoria, BC, V8P 5C2, Canada  Paul Goldsmith
Jet Propulsion Laboratory, California Institute of Technology, Pasadena CA
91109, USA  Guido Garay Departamento de Astronomía, Universidad de Chile, Las
Condes, 7591245 Santiago, Chile  Yuefang Wu Department of Astronomy, School of
Physics, Peking University, Beijing, 100871, People’s Republic of China  Kavli
Institute for Astronomy and Astrophysics, Peking University, Beijing 100871,
People’s Republic of China  Archana Soam Indian Institute of Astrophysics, II
Block, Koramangala, Bengaluru 560034, India  Alessio Traficante IAPS-INAF, Via
Fosso del Cavaliere, 100, I-00133 Rome, Italy  Isabelle Ristorcelli  3
Université de Toulouse, UPS-OMP, IRAP, F-31028 Toulouse cedex 4, France  Edith
Falgarone LPENS, Ecole Normale Supérieure, Université PSL, CNRS, Sorbonne
Université, Université de Paris, 75005 Paris, France  Huei-Ru Vivien Chen
Institute of Astronomy and Department of Physics, National Tsing Hua
University, Hsinchu 30013, Taiwan  Naomi Hirano Institute of Astronomy and
Astrophysics, Academia Sinica, No.1, Sec. 4, Roosevelt Road, Taipei 10617,
Taiwan  Yasuo Doi Department of Earth Science and Astronomy, Graduate School
of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro, Tokyo
153-8902, Japan  Woojin Kwon Department of Earth Science Education, Seoul
National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
SNU Astronomy Research Center, Seoul National University, 1 Gwanak-ro, Gwanak-
gu, Seoul 08826, Republic of Korea  Glenn J. White School of Physical
Sciences, The Open University, Walton Hall, Milton Keynes, MK7 6AA, UK  RAL
Space, STFC Rutherford Appleton Laboratory, Chilton, Didcot, Oxfordshire, OX11
0QX, UK  Anthony Whitworth School of Physics and Astronomy, Cardiff
University, The Parade, Cardiff, CF24 3AA, UK  Patricio Sanhueza National
Astronomical Observatory of Japan, National Institutes of Natural Sciences,
2-21-1 Osawa, Mitaka, Tokyo 181-8588, Japan  Astronomical Science Program, The
Graduate University for Advanced Studies, SOKENDAI, 2-21-1 Osawa, Mitaka,
Tokyo 181-8588, Japan  Mark G. Rawlings Gemini Observatory/NSF’s NOIRLab, 670
N. A‘ohōkū Place, Hilo, HI 96720, USA  East Asian Observatory, 660 N. A‘ohōkū
Place, University Park, Hilo, HI 96720, USA  Dana Alina Nazarbayev University,
Kabanbay Batyr Ave, 53, Astana 010000 Kazakhstan  Zhiyuan Ren National
Astronomical Observatories, Chinese Academy of Sciences, Datun Rd. A20,
Beijing, People’s Republic of China  CAS Key Laboratory of FAST, NAOC, Chinese
Academy of Sciences, Beijing, China; University of Chinese Academy of
Sciences, Beijing, China  Chang Won Lee Korea Astronomy and Space Science
Institute, 776 Daedeokdae-ro, Yuseong-gu, Daejeon 34055, Republic of Korea
University of Science and Technology, Korea (UST), 217 Gajeong-ro, Yuseong-gu,
Daejeon 34113, Republic of Korea  Ken’ichi Tatematsu Nobeyama Radio
Observatory, National Astronomical Observatory of Japan, National Institutes
of Natural Sciences, 462-2 Nobeyama, Minamimaki, Minamisaku, Nagano 384-1305,
Japan  Astronomical Science Program, The Graduate University for Advanced
Studies, SOKENDAI, 2-21-1 Osawa, Mitaka, Tokyo 181-8588, Japan  Chuan-Peng
Zhang National Astronomical Observatories, Chinese Academy of Sciences,
Beijing 100101, China  Guizhou Radio Astronomical Observatory, Guizhou
University, Guiyang 550000, China  Jianjun Zhou XingJiang Astronomical
Observatory, Chinese Academy of Sciences, Urumqi 830011, PR China  Key
Laboratory of Radio Astronomy, Chinese Academy of Sciences, Urumqi 830011, PR
China  Xinjiang Key Laboratory of Radio Astrophysics, Urumqi 830011, PR China
Shih-Ping Lai Institute of Astronomy and Astrophysics, Academia Sinica, No.1,
Sec. 4, Roosevelt Road, Taipei 10617, Taiwan  Derek Ward-Thompson Jeremiah
Horrocks Institute, University of Central Lancashire, Preston PR1 2HE, UK
Sheng-Yuan Liu Institute of Astronomy and Astrophysics, Academia Sinica, No.1,
Sec. 4, Roosevelt Road, Taipei 10617, Taiwan  Qilao Gu Shanghai Astronomical
Observatory, Chinese Academy of Sciences, 80 Nandan Road, Shanghai 200030,
People’s Republic of China  Eswaraiah Chakali Indian Institute of Science
Education and Research (IISER) Tirupati, Rami Reddy Nagar, Karakambadi Road,
Mangalam (P.O.), Tirupati 517 507, India  Lei Zhu  National Astronomical
Observatories, Chinese Academy of Sciences, People’s Republic of China  Diego
Mardones Departamento de Astronomía, Universidad de Chile, Casilla 36-D,
Santiago, Chile  L. Viktor Tóth Institute of Physics and Astronomy, Eötvös
Lorànd University, Pázmány Péter sétány 1/A, H-1117 Budapest, Hungary

###### Abstract

High-latitude ($|b|>30\arcdeg$) molecular clouds have virial parameters that
exceed 1, but whether these clouds can form stars has not been studied
systematically. Using JCMT SCUBA-2 archival data, we surveyed 70 fields that
target high-latitude Planck galactic cold clumps (HLPCs) to find dense cores
with density of $10^{5}$–$10^{6}$ cm-3 and size of $<0.1$ pc. The sample
benefits from both the representativeness of the parent sample and covering
densest clumps at the high column density end ($>1\times 10^{21}$ cm-2). At an
average rms of 15 $\mathrm{mJy\,beam^{-1}}$, we detected Galactic dense cores
in only one field, G6.04+36.77 (L183), while also identifying 12 extragalactic
objects and two young stellar objects. Compared to the low-latitude clumps,
dense cores are scarce in HLPCs. With synthetic observations, the densities of
cores are constrained to be $n_{c}\lesssim 10^{5}$ cm-3, should they exist in
HLPCs. Low-latitude clumps, Taurus clumps, and HLPCs form a sequence where a
higher virial parameter corresponds to a lower dense core detection rate. If
HLPCs were affected by the Local Bubble, the scarcity should favor turbulence-
inhibited rather than supernova-driven star formation. Studies of the
formation mechanism of the L183 molecular cloud are warranted.

Star formation (1569), Molecular clouds (1072), High latitude field (737)

††software: Astropy, a community developed core python package for astronomy
(Astropy Collaboration et al., 2013, 2018, 2022). Montage, funded by the
National Science Foundation under Grant Number ACI-1440620, and previously
funded by the National Aeronautics and Space Administration’s Earth Science
Technology Office, Computation Technologies Project, under Cooperative
Agreement Number NCC5-626 between NASA and the California Institute of
Technology (Jacob et al., 2010; Berriman & Good, 2017).

=30

=50

##  1 Introduction

The high latitude (HL hereafter) of the Milky Way, also called the “Underwater
Iceberg” guards its secrets about molecular gas and star formation, due in
part to the limited scope of previous CO surveys (Xu et al., 2021).
Observational challenges essentially originate from the large area of the HL,
leading to much longer integration times compared to those required for blind
surveys of the Galactic plane. The Planck satellite provides an unprecedented
all-sky census of the coldest (6–20 K with a median value of $\sim$14 K;
Planck Collaboration et al., 2011a) Galactic objects by combining the highest
frequency channels of the Planck survey 353–857 GHz (i.e., 850–350 $\mu$m)
with the far-infrared IRAS 100 $\mu$m data (Neugebauer et al., 1984; Miville-
Deschênes & Lagache, 2005). As a result, the Planck team has cataloged 13,188
Planck Galactic Cold Clumps (PGCCs; Planck Collaboration et al., 2016),
including 793 with absolute value of Galactic latitude higher than 30∘ (HLPCs
hereafter). Benefiting from the unbiased nature of its parent sample, the 793
HLPCs are the least biased sample of HL cold dust clumps, therefore serving as
a foundation for studying the properties of the HL molecular gas and
investigating the initial condition of star formation (Wu et al., 2012; Liu et
al., 2013).

Our previous work performed a 12CO/13CO/C18O (1-0) survey towards 41 Early
Cold Cores (ECCs; i.e., most reliable detections of PGCCs with signal-to-noise
ratio $>15$; Planck Collaboration et al., 2011b) with the PMO 13.7 m
millimeter-wavelength telescope (Xu et al., 2021). Although detected CO cores
have a typical density of several times $10^{4}$ cm-3, consistent with what
has been found in nearby molecular cloud cores (Myers et al., 1983; Myers &
Benson, 1983; Myers, 1983; Benson & Myers, 1983, 1989), the turbulent energy
is significantly higher than the gravitational energy, with median virial
parameters of $\sim 35$ (Xu et al., 2021). Therefore, our CO surveys unveiled
a highly turbulent, diffuse molecular gas environment as a first glimpse of
the initial conditions of star formation in the HL clouds.

Stars form in dense cores (Shu et al., 1987) with a typical size of $\lesssim
0.1$ pc and density of $10^{5}$–$10^{6}$ cm-3 (Ward-Thompson et al., 1994,
1999; Kirk et al., 2005). The CO (1-0) transitions suffer from optical
thickness and depletion at low temperature, so it is hard to probe the densest
regions of molecular clouds. For example, a high-latitude cloud L1780 shows a
cometary morphology and a CO core (Toth et al., 1995), but contains no dense
core in our survey (field G358.96+36.81). Furthermore, the angular resolution
of Planck used for the extraction of PGCCs is $\sim 5$′, corresponding to 0.3
pc at a typical distance of HL clouds of 200 pc (Xu et al., 2021), which is
marginal for resolving dense cores. Working at 850 $\mu$m with an effective
beam FWHM of 14$.\\!\\!\arcsec$6, Submillimetre Common-User Bolometer Array 2
(SCUBA-2; Dempsey et al., 2013) provides $\sim 20$ times better resolution
than the Planck, pinpointing cold dense cores inside the molecular clouds.

In this letter, we perform a systematic search for dense cores within 70 HLPCs
using the latest JCMT SCUBA-2 archival data. The sample selection and distance
estimation are summarized in Section 2. As shown in Section 3, dense cores are
only identified in one HLPC (G6.04+36.77). In Section 4, we show the
robustness of the scarcity of Galactic dense cores in HLPCs, investigate the
upper limit of the dense core density, and then discuss star formation picture
at high latitude. Finally, we give a brief summary in Section 5.

##  2 Data

###  2.1 Sample Selection

Figure 1: The sky distribution of 70 high-latitude Planck Galactic Cold Clumps
(HLPCs). The background color map is Planck 353 GHz (850 $\mu$m) emission in
cylinder projection. The 70 HLPCs are marked with green crosses. Three
foreground subregions are zoomed in toward the Orion Frontier, the MBM12
Complex, and the L134 Complex, The HLPCs in these regions cover the high
column density end ($N_{\mathrm{H}_{2}}>1.0\times 10^{21}$ cm-2). In the
zoomed-in subregions, HLPCs are marked with green open circles whose sizes are
equivalent to the size of the observing fields. The identifiers in each sub-
region are marked as green text. The names of the subregions and the number of
HLPCs are marked with white text on the upper left. The white rectangles are
the CO emission regions.

A thorough search of the SCUBA-2 850 $\mu$m data in the JCMT Science
Archive111https://www.cadc-ccda.hia-iha.nrc-cnrc.gc.ca/en/jcmt/, cross-
matching with 793 HLPCs that satisfy the latitude criterion of
$|b|>30\arcdeg$, gave 138 observing fields in total. We dismiss six of the
fields which have limited integration time or non-standard scan modes.
Different scan patterns includes Constant Velocity Daisy patterns (CV Daisy)
and Rotating Curvy Pong patterns (Curvy Pong), so the field offsets vary
significantly between different patterns. We make sure that the observing
fields cover the peak of PGCCs at 353 GHz. We also check superposition or
repetition: if two fields cover the same PGCC, we choose the one with the
higher sensitivity. After the work flow, a total of 70 SCUBA-2 fields are
selected as the sample in this work.

###  2.2 Sample Properties

70 HLPCs are shown with green crosses, overlaid on the background Planck 353
GHz (850 $\mu$m) continuum emission in Figure 1. White rectangles outline the
CO emission regions defined by Dame et al. (1987). The clump-averaged $N_{\rm
H_{2}}$ H2 column density $N_{\rm H_{2}}$ was calculated assuming a dust
emissivity model at 857 GHz (Planck Collaboration et al., 2016). The $N_{\rm
H_{2}}$ distributions of three samples, all the PGCCs, 793 high-latitude
PGCCs, and 70 HLPCs are plotted as gray, blue and orange histograms in Figure
2, respectively. The $N_{\rm H_{2}}$ span of the 70 HLPCs has been evenly
sampled, ensuring a similar distribution with its parent sample, 793 HL PGCCs.
More importantly, the studied sample includes the densest clumps at the high-
density end ($N_{\mathrm{H}_{2}}>2.0\times 10^{21}$ cm-2). Considering that
the denser clumps should be more likely to produce dense cores, we’ve covered
the complete HLPCs where dense cores could form.

Three regions with relatively higher column density, namely the Orion
Frontier, the MBM 12 Complex, and the L134 Complex, are further zoomed in with
subpanels in Figure 1. The HLPCs therein correspond to those at the high-
density end as mentioned above. The Orion Frontier contains the dark cloud
L1642, which together with the MBM 12 Complex are two famous HL clouds that
have confirmed star-forming activity (Malinen et al., 2014). The L134 complex
is another region containing several HLPCs, including the widely studied dark
cloud L183 (Lee & Myers, 1999; Lee et al., 2001; Juvela et al., 2002; Pagani
et al., 2003).

Figure 2: The gray histogram shows the distribution of 13,188 PGCCs. The blue
histogram is for 793 high-latitude PGCCs while the orange histogram is for the
70 HLPCs used in this work. A subpanel zooms in the H2 column density range of
$10^{20.8}$–$10^{22.2}$ cm-2.

###  2.3 Distance Estimation

Distance is always a difficult quantity to estimate in astronomy. Previous
studies have estimated the distances of HL molecular clouds to be 100 pc from
the velocity dispersion and the scale height of an ensemble of clouds (Blitz
et al., 1984; Magnani et al., 1985). The star counting confirms that the HL
molecular clouds are indeed nearby objects with upper limit ranges from 125 to
275 pc (Magnani & de Vries, 1986). Using Strömgren photometry, Franco (1989)
derive the distances of several HGaL clouds to be 100–230 pc. Both the small
$V_{\mathrm{lsr}}$ and the lack of a double-sine wave signature in the
distribution on the $l-V_{lsr}$ (Galactic longitude $l$) plane demonstrate
that HL molecular gas belongs to the local ISM and is too close to the Sun for
Galactic rotation to modulate the velocities (Magnani et al., 1996).

Recently, the Gaia satellite has provided new photometric measurements towards
galactic stars (Gaia Collaboration et al., 2016). Together with 2MASS and Pan-
STARRS 1 optical and near-infrared photometry, Gaia DR2 parallaxes can help to
infer distances and reddenings of $\sim$ 800 million stars. These stars trace
the reddening on a small patch of the sky, along different lines of sight and
different distance intervals, allowing us to build a 3D dust reddening map
(Green et al., 2014, 2019). In a given direction, a jump of dust reddening is
expected at a distance where there is a dust clump. Distances are estimated by
this method and are listed in column (9) of Table A1, with an average value of
$200\pm 60$ pc, indicating that HLPCs are mostly local ISM. Adopting the Eq. 1
in Xu et al. (2021) and considering that the Sun is 10 pc above the Galactic
midplane (Griv et al., 2021), the altitude $z$ from the midplane (in unit of
pc) is calculated from $z=d\sin(b)+10$ where $d$ is the distance and $b$ is
the latitude, and listed in column (10). We note that some fields may contain
extragalactic objects, so the distance should be only for foreground Galactic
dust.

##  3 Results

###  3.1 Source Extraction

We adopt the dendrogram algorithm (Rosolowsky et al., 2008) to extract dense
structures, and then measure their integrated flux, peak flux, size and
position by 2D Gaussian fitting. The details of the algorithm parameter
settings and the source extraction procedure are introduced in Appendix B.
Within the 70 input fields, we have initially detected a total of 20 sources
that belong to 15 SCUBA-2 observing fields. The field names and extracted
sources are listed in columns (1)–(2) of Table 1. Central coordinates,
standard deviation of the deconvolved major and minor axes, position angle,
integrated flux, and peak intensity are listed in columns (3)–(8).

###  3.2 Source Identification and Dense Core Definition

The CV Daisy mode of observation can produce artifacts (Liu et al., 2018; Eden
et al., 2019) which are extracted by the algorithm as false source detections.
Therefore, we additionally require that both the peak intensity after being
smoothed to the Planck beam and the total flux of the source at 353 GHz, are
lower than those of the parent PGCC. This results in two sources, G50.41-35.40
SMM1 and G197.98+33.10 SMM1, being classified as artifacts and excluded from
further analyses. We note that the flux given by the PGCC catalog should be
from the cold residual map (Planck Collaboration et al., 2016), so the
original Planck flux at 353 GHz could be even larger. However, considering the
cold nature of dense cores, this should contribute little to the warm
component.

We cross-match the true detections within 1′ using
SIMBAD222http://simbad.u-strasbg.fr/simbad/. We find that only one field,
G6.04+36.77, pointing towards the molecular cloud L183, contains three
resolved (or marginally resolved) sources, which were previously identified as
low-mass prestellar cores (Dickens et al., 2000; Pagani et al., 2003). Our
other detections are unresolved as point sources, classified as either young
stellar object (YSO) or extragalactic object (point sources) - including
gravitational lensed galaxy (LeG), BL Lacertae object (BLL), protocluster of
galaxies (PClG), active nuclei candidate (AGN), and quasar (QSO). The
determined identifiers and references are given in column (9) of Table 1.

The physical parameters of the L183 dense cores are calculated in Appendix C.
Throughout the paper, we adopt the empirical definition of dense core with
typical size of $\lesssim 0.1$ pc and density of $10^{5}$–$10^{6}$ cm-3 (Ward-
Thompson et al., 1994, 1999; Kirk et al., 2005).

##  4 Discussion

###  4.1 Dense Cores Are Scarce In HLPCs

Having only one detection among the 70 HLPCs highlights the scarcity of dense
cores at these latitudes. To further confirm this discrepancy compared to the
low-latitude ($|b|<30\arcdeg$) counterpart, 1235 observing fields from the
JCMT Large Project “SCUBA-2 Continuum Observations of Pre-protostellar
Evolution” (SCOPE; Liu et al., 2018; Eden et al., 2019) are used as a
comparison group because of the following two reasons. 1) 21 HLPC fields in
this work come from the SCOPE project so that they have been observed with
comparable sensitivities; 2) the SCOPE observations serve as a representative
sample of PGCCs, with similar distributions in distance, size, and
temperature, and with complete column density coverage over $10^{21}$ cm-2
(Liu et al., 2018).

Considering the beam dilution effects for marginally resolved sources, the
column densities are corrected by a beam filling factor $B_{\rm
ff}=(\theta_{i}^{2})/\theta_{\rm PSF}^{2}$ where $\theta_{\rm
PSF}=4\mbox{$.\\!\\!\arcmin$}3$ is the Planck beam size at 857 GHz (Planck
Collaboration et al., 2016) and $\theta_{i}$ is the intrinsic size deconvolved
from the beam. For extended sources of which intrinsic size exceeds the beam
size, $B_{\rm ff}$ is set to 1 and no correction is performed. As a result,
the corrected column densities $N^{\prime}_{\rm H_{2}}$ increase by a factor
of 1.1 on average and 9 at maximum.

In Figure 3, we present the number distributions of different samples across a
set of $N^{\prime}_{\rm H_{2}}$ bins, denoted as ${S_{\rm samp,i}}$ where $i$
indicates bin index. Specifically, the distributions for the low-latitude
SCOPE fields and the HLPC fields are depicted using the gray and blue
histograms, respectively. We also collect the number distribution of those
fields with dense cores detected $\\{S_{\rm det,i}\\}$. The dense core
detection rate (DCDR), defined as $\\{S_{\rm det,i}/S_{\rm samp,i}\\}$, is
shown with connected data points. For the low-latitude SCOPE, the DCDR
experiences a pronounced increase at a threshold of column density around
$N_{\rm H_{2}}\simeq 1.0\times 10^{21}$ cm-2, reaching 90% at the $N_{\rm
H_{2}}\simeq 5.0\times 10^{21}$ cm-2 regime. The column density threshold for
forming dense cores is consistent with what has been found in Gould Belt
clouds (e.g., Johnstone et al., 2004). The sudden peak at $3\times 10^{20}$
cm-2 likely results from the limited sample size. In contrast to the DCDR of
the low-latitude SCOPE clumps, the DCDR for the HLPCs remains zero for $N_{\rm
H_{2}}<1.0\times 10^{22}$ cm-2, until dense cores in the HLPC G6.04+36.77 are
detected.

The SCOPE serve as a gauge to tell whether the HLPCs have significantly scarce
dense cores. Given the null hypothesis that the HLPCs share a DCDR greater or
equal to that of the SCOPE, the number of the HLPC fields containing dense
cores in each bin of $N_{\rm H_{2}}\geq 10^{21}$ cm-2 can be predicted, as
$\\{S_{\rm pred,i}\\}_{\rm HLPC}$. The one-sided Mann–Whitney U test (Mann &
Whitney, 1947) is performed between the $\\{S_{\rm pred,i}\\}_{\rm HLPC}$ and
$\\{S_{\rm det,i}\\}_{\rm HLPC}$, giving a p-value of $4.8\times 10^{-3}$.
This is $\ll 0.05$, thus robustly excluding the null hypothesis. In other
words, dense cores are scarce in HLPCs compared to the low latitude.

Figure 3: The distribution of column density corrected by beam filling factor
$N^{\prime}_{\rm H_{2}}$

for low-latitude SCOPE fields (gray), the HLPC fields (blue) and Taurus SCOPE
fields (orange), respectively. The connected data points in corresponding
colors depict the DCDR in various $N_{\rm H_{2}}$ bins.

###  4.2 What Does “Dense” Mean?

The scarcity of detection does not necessarily indicate the scarcity of dense
cores, primarily due to two factors: 1) the limited sensitivity of identifying
sources and 2) the absence of large-scale flux in the SCUBA-2 data processing.
Therefore, to understand what the scarcity of dense cores means, it is
essential to clarify what “dense” means.

Figure 4: The peak intensity $\widetilde{I}^{\rm peak}_{\nu}$ observed by
SCUBA-2 across parameter space defined by the flat radius $R_{\rm
flat}\in[10^{3},10^{4.5}]$ au and the central density
$n_{c}\in[10^{3.5},10^{6}]$ cm-3. The white contours mark $1\sigma$,
$3\sigma$, and $9\sigma$ levels, while the black contours trace the same loci
of synthetic model (no large-scale flux filtered out). The green dashed line
delineates the flat inner radius limits $R_{\rm flat}\in[2500,20000]$. The
shaded gray region indicates the permissible parameter interval of undetected
prestellar cores should they exist. Blue and red stars show the L183
prestellar cores and other previously detected prestellar cores (Ward-Thompson
et al., 1994, 1999; Kirk et al., 2005). The orange rectangles show two weakly
detected cores in our fields. The green triangles show cores detected by
Herschel in several HLPCs (Montillaud et al., 2015), but undetected by
SCUBA-2.

Prestellar cores are observed to have flat inner density gradients that
approach $\rho\sim r^{-2}$ beyond a few thousand au (Ward-Thompson et al.,
1994, 1999; Kirk et al., 2005), which can be reproduced by a nonmagnetic and
Plummer-like model (Whitworth & Ward-Thompson, 2001) as,

$n_{\rm H_{2}}(r)=\frac{n_{c}}{1+(r/R_{\rm flat})^{2}},$ (1)

where $n_{c}$ is the central H2 number density and $R_{\rm flat}$ is the flat
inner radius. The column density profile of such a model core has the
analytical form of

$N_{\rm H_{2}}(p)=\frac{2n_{c}R_{\rm flat}}{(1+p^{2}/R_{\rm
flat}^{2})^{1/2}}\times\tan^{-1}\left[\frac{(R_{\rm
out}^{2}-p^{2})^{1/2}}{(R_{\rm flat}^{2}+p^{2})^{1/2}}\right],$ (2)

where $p$ is the distance from core center in the plane of the sky (Dapp &
Basu, 2009) and $R_{\rm out}=0.2$ pc $\simeq 40000$ au defines the boundary of
core.

Using the GBT+JVLA combined data, the temperatures of the three prestellar
cores are observed to have a minor decrease toward the center of the core
$\lesssim 2000$ au (Lin et al., 2023). Therefore, we consider a constant
temperature profile as $T(r)=T_{0}=10$ K in the following discussion.

Assuming optically thin dust emission, the column density can be used to
synthesize model intensity as,

$I_{\nu}(p)=\frac{N_{\rm H_{2}}(p)\Omega\mu_{\rm H_{2}}m_{\rm
H}\kappa_{\nu}B_{\nu}(T_{\rm dust})}{\mathcal{R}},$ (3)

where $\Omega=\pi\theta_{\rm beam}^{2}/4\ln 2$ measures the solid angle (in
unit of radian) per JCMT beam (with FWHM of $\theta_{\rm beam}$), $\mu_{\rm
H_{2}}=2.81$ is the molecular weight per hydrogen molecule (Evans et al.,
2022), $m_{\rm H}$ is the mass of a hydrogen atom, $\kappa_{\nu}=1.22$ cm2 g-1
(Beckwith et al., 1990) is the dust opacity at frequency of $\nu=350$ GHz
($\sim 850$ $\mu$m), $B_{\nu}(T_{\rm dust})$ is the Planck function at a given
dust temperature $T_{\rm dust}$, and $\mathcal{R}=100$ is the gas-to-dust mass
ratio.

Now we synthesize the SCUBA-2 image $\widetilde{I}_{\nu}(x,y)$ by adding a
high-frequency filter $\sqrt{u^{2}+v^{2}}>\xi$, where $\xi$ corresponds to
$200\arcsec$ in the frequency space (Mairs et al., 2015),

$\displaystyle\widetilde{I}_{\nu}(x,y)=\iint_{\\{\sqrt{u^{2}+v^{2}}>\xi\\}}J_{\nu}(u,v)e^{i2\pi(ux+vy)}\mathrm{d}u\,\mathrm{d}v,$
(4)

where $J_{\nu}(u,v)=\iint I_{\nu}(x,y)e^{-i2\pi(ux+vy)}\mathrm{d}x\mathrm{d}y$
is the synthetic model in the Fourier frequency space. As a result, the
synthetic observed intensity $\widetilde{I}_{\nu}(x,y)$ can simulate the
observed large-scale missing flux at the SCUBA-2 data processing.

In Figure 4, the background color map shows observed peak intensities
$\widetilde{I}^{\rm peak}_{\nu}=\widetilde{I}_{\nu}(0,0)$ across the 2-D
parameter space of flat radius $R_{\rm flat}\in[10^{3},10^{4.5}]$ au and
central density $n_{c}\in[10^{3.5},10^{6}]$ cm-3. The prestellar cores have
been reported to have $R_{\rm flat}>2500$ au (Ward-Thompson et al., 1999; Kirk
et al., 2005), which are delineated by the left green dashed line in Figure 4.
The right green dashed line marks 20,000 au, which corresponds to 0.1 pc.

Consistent with the criteria in the source extraction algorithm (see Appendix
B), a threshold of $3\sigma$ is adopted to constrain the upper limit of
$\widetilde{I}^{\rm peak}_{\nu}$. As a result, the gray shaded region traces
the permissible parameter interval for an undetected core in our observations.
In other words, if such cores exist, they should have $n_{c}<10^{5}$ cm-3,
which is considerably less dense than those that have been identified in
nearby low-mass cloud cores (Ward-Thompson et al., 1999; Kirk et al., 2005).

To further demonstrate this upper limit of density, we smooth the images to a
resolution of 20″. With better sensitivity, two new cores (on in HLPC
G159.41-34.37 and one in G161.87-35.76) are identified by the same algorithm
and parameter input, which are called weakly detected cores. They have radii
of about 6700 and 7500 au and averaged density of $2.8\times 10^{4}$ cm-3 and
$3.1\times 10^{4}$ cm-3, respectively. The two cores are labelled as orange
rectangles in Figure 4. We also retrieve Herschel cold cores in L134 (HLPCs
G4.13+35.75 and G4.17+36.67 in our survey), MBM12 (G159.21-34.28,
G158.51-33.99, G159.14-33.79, G159.23-34.51, G159.41-34.37, G159.66-34.31),
L1642 (G210.90-36.55), and LDN1780 (G358.96+36.81, G359.21+36.89) from
Montillaud et al. (2015) which are labelled as green triangles. As they all
lie in the gray region, below our sensitivity limit, these cold cores are not
dense enough for detection and are consistently below the density limit of
$10^{5}$ cm-3. As noted by Ward-Thompson et al. (2016), SCUBA-2 selects the
densest cores from a population at a given temperature, which makes SCUBA-2
ideal for identifying those cores in Herschel catalogues that are closest to
forming stars. So it is of great interest to study how these low-density cores
form and whether they can still form stars or are transient objects.

As indicated by the black contours, peak intensity of a synthetic model
$I^{\rm peak}_{\nu}$ is always below the corresponding $\widetilde{I}^{\rm
peak}_{\nu}$ outlined by white contours. The difference reflects the missing
large-scale flux, which increases in importance from 0.06 to 0.31 dex with
$R_{\rm flat}$ from 2500 to 20,000 au. Therefore, if we don’t consider missing
flux, the density limit can be even lower, especially for those cores with
larger flat radius.

###  4.3 Star Formation at High Galactic Latitude

Low density and high virial parameter lead to a challenge for direct
gravitational collapse and then star formation of HL clouds. Observationally,
it is consistent with infrared cirrus which is thought to be hostile to star
formation (Low et al., 1984) and the dispersed populations of pre-main-
sequence stars (see review by McGehee, 2008). Recently, a clear decreasing
trend of N2H+ (1–0) and C2H (1–0) detection rates with latitude is found by Xu
et al. (2021). Besides, HCN (1–0) and HCO+ (1–0) line survey by Braine et al.
(2023) reveals that HL molecular clouds have lower dense gas fractions
compared to those in the Galactic plane. Theoretically, based on Jeans mass
arguments, these low-density turbulent clouds have molecular gas mass lower
than the turbulent Jeans mass (cf. Table 5 in Xu et al., 2021), therefore
unable to fragment into dense dust cores, or protostellar embryos, which
agrees with the scarcity of dense cores observed by SCUBA-2.

Previous studies have reported that the virial parameters of the PGCCs in the
Taurus region (Taurus clumps hereafter) are predominantly greater than 1, with
a median value of approximately 9 (Meng et al., 2013). This value is
considerably lower than the median virial parameter of HLPCs, which stands at
about 35. The Taurus clumps covered by the SCOPE project are designated as the
Taurus SCOPE subsample. Figure,3 displays the number distribution and the DCDR
for the Taurus SCOPE in orange. In the same way as above, the Mann-Whitney U
test gives a p-value of $0.042<0.05$, thus favoring that the DCDR of the
Taurus clumps is statistically larger than that of HLPCs. Interestingly, the
Taurus clumps also exhibit a significantly smaller DCDR compared to the low-
latitude SCOPE clumps, as evidenced by a Mann-Whitney U test p-value of
$5.4\times 10^{-3}$. This indicates that dense cores within the Taurus clumps
are relatively rarer compared to other SCOPE clumps. Consequently, the Taurus
clumps occupy an intermediate position between the HLPCs and low-latitude
SCOPE clumps in terms of DCDR and virial parameter. The observed trend of
decreasing DCDR with increasing virial parameter further substantiates the
link between core formation efficiency and the dynamic state of the gas, as
previously suggested (Eden et al., 2019).

HLPCs have a distance of 200 pc which is highly consistent with the radius of
the Local Bubble (LB) created by supernovae (Zucker et al., 2022). The LB is
reported to expand and sweep up the ambient interstellar medium into a shell
that has now fragmented and collapsed into the most prominent nearby molecular
clouds. Interestingly, Zucker et al. (2022) also found that the Taurus star
formation region is very likely being compressed by two super bubbles: the
local super bubble and the smaller Per-Tau super bubble. If so, it’s probable
that the formation of dense cores can be hindered by supernova shocks in the
solar neighborhood. If the HLPCs and the Taurus clumps are on the shell, the
scarcity of dense cores should favor turbulence-inhibited rather than
supernova-driven star formation.

On the other hand, the scarcity itself is what gives the only detection (L183)
in our survey, as well as a few other clouds (such as MBM 12 and 20), unique
status. The capacity of these high-latitude clouds to form cold molecular
cores and young stars could arise from a confluence of conditions including
variations in the interstellar radiation field, changes in dust grain size and
chemistry, the occurrence of shocks, and transient events in the ISM (McGehee,
2008). Consequently, in-depth explorations of the physical and chemical
processes within these high-latitude dense cores, for example the L183 dense
cores, are merited.

##  5 Conclusion

We performed a JCMT SCUBA-2 archival investigation of 70 fields towards High-
Latitude Planck Galactic Cold Clumps (HLPCs) to search for dense cores. The
sample benefits from being representative of the total HLPC population at low
column density ($<2\times 10^{21}$ cm-2) and covering the densest clumps at
the high column density end ($>1\times 10^{21}$ cm-2). Using the dust
reddening in 3D map, the distances of the HLPCs are estimated to be 110–410 pc
with a mean value of 200($\pm$60) pc. A total of 17 SCUBA-2 sources are
identified from a mean noise rms of 15 $\mathrm{mJy\,beam^{-1}}$. Only one
field G6.04+36.77 (L183) contains three dense Galactic cores. The other 14
unresolved sources include 12 extragalactic objects and two Galactic young
stellar objects.

Compared to the low-latitude SCOPE clumps and the Taurus clumps (at a similar
distance to HLPCs), the dense core detection rate (DCDR) of HLPCs is
significantly lower at the high column density end ($>1\times 10^{21}$ cm-2).
Statistical tests verify the scarcity of dense cores in HLPCs. With synthetic
observations of known dense cores, the central density of any undetected dense
cores is constrained to be $n_{c}\lesssim 10^{5}$ cm-3, should they exist in
HLPCs. The observed scarcity of dense cores aligns with the low-density
turbulent environment in HLPCs, as proposed in previous far-infrared and CO
line surveys. If the HLPCs and the Taurus clumps were on the shell of the
Local Bubble, the scarcity of dense cores should favor turbulence-inhibited
rather than supernova-driven star formation. Furthermore, the scarcity also
calls for further study on the formation mechanism of L183 dense cores.

## Acknowledgment

This work has been supported by the National Key R&D Program of China (No.
2022YFA1603102, 2019YFA0405100), the National Science Foundation of China
(12033005, 11973013), and the China Manned Space Project (CMS-CSST-2021-A09,
CMS-CSST-2021-B06), and the China-Chile Joint Research Fund (CCJRF No. 2211).
CCJRF is provided by Chinese Academy of Sciences South America Center for
Astronomy (CASSACA) and established by National Astronomical Observatories,
Chinese Academy of Sciences (NAOC) and Chilean Astronomy Society (SOCHIAS) to
support China-Chile collaborations in astronomy. T.L. acknowledges the support
by the international partnership program of Chinese academy of sciences
through grant No.114231KYSB20200009, and Shanghai Pujiang Program 20PJ1415500.
This research was carried out in part at the Jet Propulsion Laboratory, which
is operated by the California Institute of Technology under a contract with
the National Aeronautics and Space Administration (80NM0018D0004). This work
is sponsored (in part) by the Chinese Academy of Sciences (CAS), through a
grant to the CAS South America Center for Astronomy (CASSACA) in Santiago,
Chile. D.J. is supported by NRC Canada and by an NSERC Discovery Grant. G.G.
acknowledges support from the ANID BASAL project FB210003. E.F. acknowledges
support from the European Council, under the European Community’s Seventh
framework Programme, through the Advance Grant MIST (FP7/2017-2024, No.
742719). P.S. was partially supported by a Grant-in-Aid for Scientific
Research (KAKENHI Number JP22H01271 and JP23H01221) of JSPS. C.W.L. is
supported by the Basic Science Research Program through the National Research
Foundation of Korea (NRF) funded by the Ministry of Education, Science and
Technology (NRF-2019R1A2C1010851), and by the Korea Astronomy and Space
Science Institute grant funded by the Korea government (MSIT) (Project No.
2023-1-84000). G.J.W. gratefully acknowledges receipt of an Emeritus
Fellowship from The Leverhulme Trust. M.J. acknowledges support from the
Research Council of Finland grant 348342. The work of M.G.R. is supported by
NOIRLab, which is managed by the Association of Universities for Research in
Astronomy (AURA) under a cooperative agreement with the National Science
Foundation. N.H. acknowledge support from the National Science and Technology
Council (NSTC) of Taiwan with grant NSTC 111-2112-M-001-060.

This research used the facilities of the Canadian Astronomy Data Centre
operated by the National Research Council of Canada with the support of the
Canadian Space Agency. The James Clerk Maxwell Telescope is operated by the
East Asian Observatory on behalf of The National Astronomical Observatory of
Japan; Academia Sinica Institute of Astronomy and Astrophysics; the Korea
Astronomy and Space Science Institute; the National Astronomical Research
Institute of Thailand; Center for Astronomical Mega-Science (as well as the
National Key R&D Program of China with No. 2017YFA0402700). Additional funding
support is provided by the Science and Technology Facilities Council of the
United Kingdom and participating universities and organizations in the United
Kingdom and Canada. Additional funds for the construction of SCUBA-2 were
provided by the Canada Foundation for Innovation.

Table 1: Detected Sources Catalog

Field | Source | RA | DEC | $\sigma_{\mathrm{maj}}\times\sigma_{\mathrm{min}}$ | $F_{\mathrm{int}}$ | $I_{\mathrm{peak}}$ | Identifier[refs]a | Classb
---|---|---|---|---|---|---|---|---
|  | deg | deg | arcsec2 | $\mathrm{Jy}$ | $\mathrm{mJy\,beam^{-1}}$ |  |
G6.04+36.77 | SMM1 | 238.5361 | -2.8732 | 34.2$\times$17.2 | 3.36 | 218.4 |  [0pt][c]Position C[1] Region 3[2] | PSC
| SMM2 | 238.5025 | -2.8786 | 10.2$\times$7.4 | 0.13 | 57.4 |  [0pt][c]Position W[1] Region 4[2] | PSC
| SMM3 | 238.5404 | -2.8154 | 13.5$\times$10.0 | 0.35 | 64.2 |  [0pt][c]Position N[1] Region 5[2] | PSC
G45.12+61.11 | SMM1 | 225.6511 | 29.3460 | point | 0.08 | 111.2 |  [0pt][c]PLCK G045.1+61.1[3,4] | LeG
G50.41-35.40 | SMM1 | 321.7839 | -2.7151 | 10.3$\times$8.3 | 0.52 | 148.5 | - | Artifact
G53.44-36.25 | SMM1 | 323.7980 | -1.0478 | point | 0.09 | 111.8 | SMMJ2135-0102[5] | LeG
G92.49+42.88 | SMM1 | 242.3232 | 60.7542 | point | 0.08 | 110.4 | PLCK G092.5+42.9[3,4] | LeG
G152.54-47.36 | SMM1 | 32.8050 | 10.8598 | point | 0.89 | 942.0 | J021113.1+105134[6] | BLL
G157.44+30.33 | SMM1 | 113.3787 | 117.2158 | point | 0.07 | 74.8 | PLCKESZ[7] | ClG
G197.98+33.10 | SMM1 | 128.3946 | 26.1982 | 36.8$\times$27.7 | 6.29 | 176.6 | - | Artifact
G200.62+46.09 | SMM1 | 143.0981 | 27.4163 | point | 0.06 | 75.1 | PLCK G200.6+46.1[3] | LeG
G204.99+30.38 | SMM1 | 127.6930 | 19.6131 | point | 0.014 | 20.5 | Planck18p194-0[8] | PClG
| SMM2 | 127.7268 | 19.6251 | point | 0.009 | 15.4 | Planck18p194-1[8] | PClG
| SMM3 | 127.6705 | 19.6631 | point | 0.008 | 14.2 | Planck18p194-3[8] | PClG
G210.90-36.55 | SMM1 | 68.7600 | -14.2287 | point | 0.05 | 64.1 |  [0pt][c]GCVS EW Eri[9] MJR2015 1752[10] |  [0pt][c]V* Y*O
| SMM2 | 68.7080 | -14.2195 | point | 0.08 | 82.1 |  [0pt][c]HH123[11] MJR2015 1751[10] |  [0pt][c]HH Y*O
G211.62+32.23 | SMM1 | 131.7098 | 15.0943 | point | 0.11 | 125.8 | J084650.1+150547[12] | AGN?
G228.99+30.91 | SMM1 | 137.2924 | 1.3599 | point | 0.23 | 274.9 | 4C 01.24B[13] | QSO
G343.12+58.61 | SMM1 | 212.5190 | 2.0516 | point | 0.04 | 68.4 | J141004.6+020306[14] | BLL

aafootnotetext:  [1] Dickens et al. (2000); [2] Karoly et al. (2020); [3]
Cañameras et al. (2015); [4] Frye et al. (2019); [5] Swinbank et al. (2010);
[6] Healey et al. (2008); [7] Planck Collaboration et al. (2014); [8]
MacKenzie et al. (2017); [9] Samus’ et al. (2017); [10] Montillaud et al.
(2015); [11] Reipurth & Heathcote (1990); [12] Truebenbach & Darling (2017);
[13] Wright et al. (2009); [14] Plotkin et al. (2008);

bbfootnotetext: Classification according to references. PSC–prestellar core.
LeG–gravitational lensed galaxy. Artifact–probable artifact by JCMT data
reduction pipeline. BLL–BL Lacertae object. ClG–cluster of galaxies.
PClG–proto-cluster of galaxies. V*–variable star. Y*O–young stellar object.
HH–Herbig-Haro object. AGN?–active galactic nuclei candidate. QSO–Quasar.

Note. — The HLPC fields are listed in column (1). The extracted sources are
named as SMM$\mathcal{X}$, as listed in column (2). The equatorial coordinates
of Right Accession (RA) and Declination (DEC) in Epoch J2000 are listed in
columns (3) and (4). The deconvolved standard deviation along the major and
minor axis are listed in columns (5), and marked as “point” if the source is
unresolved as a point source. The integrated flux and peak intensity are
listed in columns (6) and (7). Identifier(s) and classifications retrieved
from SIMBAD are listed in columns (8) and (9).

##  Appendix A SCUBA-2 Observation Archive

Table A1 presents the information for JCMT SCUBA-2 observations toward 70 High
Latitude Planck Galactic Cold Clumps (HLPCs). We sort the observations by
Galactic longitude and number the fields from 1 to 70. The serial number and
the name of HLPC are listed in columns (1) and (2). The equatorial coordinates
Right Accession (RA) and Declination (DEC) of the field center in Epoch J2000
are listed in columns (3) and (4). The project ID and scan pattern of the JCMT
SCUBA-2 observation are listed in columns (5) and (6). The angular offset,
which is defined by the angular distance from the field center to the center
of the corresponding PGCC, is listed in column (7). The rms noise of the field
is listed in column (8). As mentioned in Section 2.3, the estimated distances
and altitude are listed in columns (9)–(10).

Table A1: Parameters of 70 High-latitude Planck Galactic Cold Clumps

No. | Field | RA | DEC | Project ID | Scan Patterna | Offset | rms | Distance | Altitude
---|---|---|---|---|---|---|---|---|---
|  | deg | deg |  |  | arcmin | $\mathrm{mJy\,beam^{-1}}$ | pc | pc
1 | G4.13+35.75 | 238.3879 | -4.6406 | M14AU35 | Curvy Pong | 2.17 | 6.2 | 140 | 90
2 | G4.17+36.67 | 237.6817 | -4.0717 | M15AI05 | CV Daisy | 0.55 | 13.8 | 130 | 87
3 | G4.55+36.73 | 237.8058 | -3.7993 | M15AI05 | CV Daisy | 1.55 | 14.1 | 130 | 88
4 | G4.80+37.02 | 237.7933 | -3.4799 | M15AI05 | CV Daisy | 4.53 | 13.7 | 130 | 88
5 | G5.70+36.84 | 238.31 | -3.0125 | M16AL003 | CV Daisy | 0.01 | 15.9 | 120 | 83
6 | G6.04+36.77 | 238.5362 | -2.8793 | M16AL003 | CV Daisy | 2.38 | 9.6 | 120 | 83
7 | G27.31+37.33 | 246.8812 | 11.9261 | M15AI57 | Curvy Pong | 2.73 | 13.7 | 190 | 128
8 | G37.52+44.57 | 242.6992 | 21.7625 | M15AI57 | Curvy Pong | 1.61 | 24.8 | 120 | 96
9 | G45.12+61.11 | 225.65 | 29.3475 | M13AC22 | CV Daisy | 1.03 | 13.0 | 180 | 170
10 | G45.16-36.19 | 320.2862 | -6.7184 | M15AI57 | Curvy Pong | 0.64 | 15.9 | 370 | -206
11 | G48.63+34.42 | 256.0433 | 27.17 | M15AI57 | Curvy Pong | 1.08 | 14.0 | 220 | 133
12 | G48.88+30.61 | 260.2037 | 26.2654 | M15AI57 | Curvy Pong | 0.57 | 10.2 | 240 | 134
13 | G50.41-35.40 | 321.7487 | -2.6816 | M14AU02 | CV Daisy | 1.67 | 25.0 | 240 | -131
14 | G53.44-36.25 | 323.7992 | -1.0448 | M15AI29 | CV Daisy | 0.6 | 5.7 | 260 | -143
15 | G55.83-41.59 | 329.223 | -2.5211 | M19BP010 | CV Daisy | 2.3 | 21.3 | 170 | -105
16 | G63.65+47.67 | 241.9708 | 40.0444 | M13AC22 | CV Daisy | 1.25 | 3.7 | 190 | 153
17 | G92.49+42.88 | 242.3242 | 60.7558 | M13AC22 | CV Daisy | 0.81 | 15.0 | 240 | 176
18 | G93.60+55.86 | 221.0292 | 54.3658 | M13AC22 | CV Daisy | 0.81 | 4.6 | - | -
19 | G96.05-50.34 | 355.8812 | 8.9585 | M15AI57 | Curvy Pong | 0.79 | 20.1 | 170 | -123
20 | G105.07-38.06 | 357.7146 | 22.7272 | M16AL003 | CV Daisy | 2.32 | 18.3 | 240 | -141
21 | G106.71-36.53 | 358.5827 | 24.566 | M16AL003 | CV Daisy | 1.61 | 20.0 | 190 | -105
22 | G108.74-52.67 | 4.2204 | 9.2853 | M15AI57 | Curvy Pong | 2.6 | 21.4 | 190 | -144
23 | G114.26-51.70 | 7.4162 | 10.7958 | M15AI57 | Curvy Pong | 0.87 | 26.3 | 210 | -151
24 | G118.25-52.70 | 9.9721 | 10.0741 | M15AI57 | Curvy Pong | 0.6 | 25.4 | 190 | -144
25 | G126.65-71.45 | 14.0508 | -8.5967 | M14AU02 | CV Daisy | 0.96 | 10.1 | 180 | -164
26 | G128.30-69.65 | 14.7304 | -6.8572 | M15AI57 | Curvy Pong | 0.48 | 13.4 | 160 | -143
27 | G128.76-69.46 | 14.9083 | -6.6946 | M15AI57 | Curvy Pong | 1.1 | 13.6 | 170 | -152
28 | G128.94-46.39 | 17.1279 | 16.3175 | M16AL003 | CV Daisy | 3.56 | 32.1 | 190 | -130
29 | G132.04-45.20 | 19.5372 | 17.2079 | M16AL003 | CV Daisy | 1.8 | 29.0 | 260 | -174
30 | G133.71-46.64 | 20.5246 | 15.6283 | M19BP010 | CV Daisy | 2.17 | 25.8 | 180 | -123
31 | G135.11-49.44 | 21.0113 | 12.685 | M19BP010 | CV Daisy | 4.59 | 24.2 | 230 | -165
32 | G138.36-69.45 | 18.2708 | -7.2253 | M15AI57 | Curvy Pong | 0.74 | 17.5 | 160 | -143
33 | G148.45+37.96 | 136.1146 | 65.9864 | M19BP010 | CV Daisy | 3.05 | 20.5 | 330 | 210
34 | G152.54-47.36 | 32.7862 | 10.8591 | M14AU02 | CV Daisy | 0.96 | 8.8 | 180 | -125
35 | G157.44+30.33 | 117.2383 | 59.6949 | M15AI29 | CV Daisy | 1.35 | 9.2 | 240 | 133
36 | G158.51-33.99 | 43.7871 | 20.1953 | M16AL003 | CV Daisy | 0.0 | 14.0 | 220 | -112
37 | G158.75-33.31 | 44.3617 | 20.6131 | M16AL003 | CV Daisy | 2.54 | 17.8 | 230 | -117
38 | G158.86-34.19 | 43.9292 | 19.8755 | M16AL003 | CV Daisy | 0.01 | 15.0 | 240 | -127
39 | G159.14-33.79 | 44.3671 | 20.0704 | M16AL003 | CV Daisy | 0.0 | 14.8 | 220 | -111
40 | G159.21-34.28 | 44.1358 | 19.6369 | M15BI061 | CV Daisy | 0.0 | 19.2 | 240 | -128
41 | G159.23-34.51 | 44.0096 | 19.4369 | M15BI061 | CV Daisy | 0.0 | 19.2 | 240 | -128
42 | G159.41-34.37 | 44.1917 | 19.4608 | M15BI061 | CV Daisy | 1.95 | 13.8 | 210 | -106
43 | G159.58-32.83 | 45.2717 | 20.6667 | M16AL003 | CV Daisy | 0.39 | 17.7 | 210 | -101
44 | G159.66-34.31 | 44.4433 | 19.3998 | MJLSY14B | CV Daisy | 0.11 | 14.2 | 190 | -99
45 | G161.43-35.60 | 44.9392 | 17.5759 | MJLSY14B | CV Daisy | 4.28 | 14.2 | 180 | -97
46 | G161.67-35.92 | 44.8883 | 17.1405 | M16AL003 | CV Daisy | 0.01 | 15.9 | 180 | -97
47 | G161.87-35.76 | 45.1096 | 17.1912 | M14AU02 | CV Daisy | 1.02 | 15.4 | 160 | -85
48 | G174.35-39.98 | 50.0233 | 7.6149 | M14AU02 | CV Daisy | 1.06 | 11.2 | 150 | -89
49 | G191.73-83.41 | 19.6517 | -24.5784 | M14AC02 | CV Daisy | 2.36 | 2.4 | 180 | -172
50 | G197.98+33.10 | 128.4412 | 26.1953 | M14AU02 | CV Daisy | 0.44 | 17.2 | 260 | 151
51 | G200.62+46.09 | 143.1058 | 27.3923 | M14AC02 | CV Daisy | 0.37 | 3.8 | - | -
52 | G203.57-30.09 | 71.9871 | -5.9299 | M16AL003 | CV Daisy | 0.0 | 12.5 | 220 | -99
53 | G204.99+30.38 | 127.7183 | 19.6509 | M13BU09 | CV Daisy | 1.48 | 2.4 | 240 | 133
54 | G210.90-36.55 | 68.7596 | -14.2279 | M15BI041 | CV Daisy | 2.37 | 5.1 | 150 | -77
55 | G210.90+63.39 | 163.2525 | 24.9403 | M15AI57 | Curvy Pong | 1.75 | 15.2 | 110 | 108
56 | G211.62+32.23 | 131.6992 | 15.1041 | M14AU02 | CV Daisy | 0.71 | 13.0 | 310 | 174
57 | G220.55+60.12 | 161.2271 | 19.6588 | M14AU02 | CV Daisy | 0.95 | 12.9 | 150 | 144
58 | G228.99+30.91 | 137.2925 | 1.3597 | M14AU15 | CV Daisy | 2.63 | 9.6 | 410 | 221
59 | G235.60+38.28 | 146.4071 | 0.7633 | M15AI57 | Curvy Pong | 1.9 | 16.7 | 180 | 123
60 | G235.67+38.00 | 146.2854 | 0.5485 | M15AI57 | Curvy Pong | 2.83 | 16.7 | 180 | 123
61 | G240.03+68.75 | 173.2117 | 16.1356 | M15AI57 | Curvy Pong | 1.59 | 14.7 | 130 | 131
62 | G251.29+73.32 | 179.3175 | 16.1378 | M15AI57 | Curvy Pong | 1.41 | 15.3 | 120 | 127
63 | G259.63+31.89 | 155.3671 | -17.5 | MJLSY01 | Curvy Pong | 32.99 | 38.8 | 240 | 139
64 | G343.12+58.61 | 212.5196 | 2.0519 | M14AU15 | CV Daisy | 2.32 | 9.3 | 140 | 127
65 | G356.76+32.82 | 236.6396 | -11.2159 | M15AI57 | Curvy Pong | 1.88 | 29.4 | 150 | 89
66 | G356.93+30.19 | 238.7129 | -12.8761 | M15AI57 | Curvy Pong | 0.98 | 28.8 | 180 | 102
67 | G357.11+30.06 | 238.8917 | -12.8652 | M15AI57 | Curvy Pong | 0.7 | 26.7 | 160 | 92
68 | G357.38+30.60 | 238.6437 | -12.3195 | M15AI57 | Curvy Pong | 0.51 | 25.0 | 150 | 84
69 | G358.96+36.81 | 234.8908 | -7.1984 | M15AI57 | Curvy Pong | 1.73 | 27.5 | 130 | 88
70 | G359.21+36.89 | 234.9154 | -6.9746 | M15AI57 | Curvy Pong | 1.4 | 14.2 | 120 | 83

aafootnotetext: CV Daisy = Constant Velocity Daisy; Curvy Pong = Rotating
Curvy Pong.

bbfootnotetext: The rms noise within the “cut-off radius”.

Note. — The serial number and the name of HLPC are listed in columns (1) and
(2). The equatorial coordinates Right Accession (RA) and Declination (DEC) of
the field center in Epoch J2000 are listed in columns (3) and (4). The project
ID and scan pattern of the JCMT SCUBA-2 observation are listed in columns (5)
and (6). The angular offset, which is defined by the angular distance from the
field center to the center of the corresponding PGCC, is listed in column (7).
The rms noise of the field is listed in column (8). The distance derived from
dust map is listed in column (9). The altitude from the Galactic mid-plane is
listed in column (10). This table is available in its entirety in machine-
readable form.

##  Appendix B Source Extraction

To avoid large marginal noise features masquerading as sources, we set a “cut-
off radius” within which we estimate noise and extract sources for each field.
The “cut-off radius” depends on FoV. For the CV Daisy observation mode, the
radius is set to 5′. For Curvy Pong, the radius is set 10′. One exception is
the field 63 with FoV$\sim 90\arcmin$, so we set the diameter to be $\sim
67$′. We carefully select emission-free pixels and take the root mean square
(rms) as a uniform noise $\sigma$ in each field (i.e., column (8) of Table
A1). An intensity threshold of $3\sigma$, a step of $2\sigma$, and a minimum
number of pixels (12 in our case) slightly larger than those contained in a
JCMT beam are used for the input of the algorithm. In the output, “leaves” are
the smallest structures and then defined as detected sources or sources
hereafter.

Figure B1 displays all of the SCUBA-2 fields towards HLPCs. The “cut-off
radius” utilized for source extraction is demarcated by the black dashed
circles. The red contours demarcate the mask of extracted sources, while the
outcomes of the 2D Gaussian fitting are visualized through orange ellipses.

Figure B1: Full atlas for 70 HLPCs in SCUBA-2 observations. The “cut-off
radius” utilized for source extraction is demarcated by the black dashed
circles. The red contours demarcate the mask of extracted sources, while the
outcomes of the 2D Gaussian fitting are visualized through orange ellipses.
The complete figure set (6 images) will be available in the online journal.

##  Appendix C L183 Prestellar Cores

Assuming that total emission $F_{\rm int}$ in column (6) of Table 1 is dust
blackbody emission, then the mass of the three prestellar cores in L183 can be
derived from,

$M=\frac{F_{\rm int}\mathcal{R}D^{2}}{\kappa_{\nu}B_{\nu}(T_{\rm dust})},$
(C1)

where $D$ is distance of 120 pc and $T_{\rm dust}$ is estimated from the
temperature map which is derived from pixelwise SED fitting by Karoly et al.
(2020). As a result, $M_{1}=1.8$ $M_{\odot}$, $M_{2}=0.055$ $M_{\odot}$, and
$M_{3}=0.19$ $M_{\odot}$. The mass of SMM1 is consistent with what has been
derived in Karoly et al. (2020), but the masses of SMM2 and SMM3 are much
smaller. The reason is likely the 6 times better sensitivity in Karoly et al.
(2020) than ours, resulting in more extended emission being included.

The physical radius $R$ can be derived from deconvolved size by
$\eta\sqrt{\sigma_{\rm maj}\sigma_{\rm min}}\times D$ where $\eta=2.4$
(Rosolowsky et al., 2010). We obtain $R_{1}=7000$ au, $R_{2}=2500$ au, and
$R_{3}=3300$ au. And the averaged volume density of molecular hydrogen can be
calculated assuming a sphere as,

$\bar{n}({\rm H_{2}})=\frac{3M_{i}}{4\pi R_{i}^{3}\mu_{\rm H_{2}}m_{\rm
H}},\hskip 14.22636pti\;=\;1,\;2,\;3.$ (C2)

So we derive $\bar{n}_{1}=1.7\times 10^{5}$ cm-3, $\bar{n}_{2}=1.0\times
10^{5}$ cm-3, and $\bar{n}_{3}=1.6\times 10^{5}$ cm-3, which are all
consistent with values in Karoly et al. (2020).

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