A&A 633, A147 (2020)
https://doi.org/10.1051/0004-6361/201936096

Astronomy&
Astrophysics

c ESO 2020

The Forgotten Quadrant Survey
12

CO and 13 CO (1–0) survey of the Galactic plane in the range
220◦ < l < 240◦ −2◦. 5 < b < 0◦?

M. Benedettini1 , S. Molinari1 , A. Baldeschi2,1 , M. T. Beltrán3 , J. Brand4 , R. Cesaroni3 , D. Elia1 , F. Fontani3 ,M. Merello5,1 , L. Olmi3 , S. Pezzuto1 , K. L. J. Rygl4 , E. Schisano1 , L. Testi3,6 , and A. Traficante11

INAF – Istituto di Astrofisica e Planetologia Spaziali, Via Fosso del Cavaliere 100, 00133 Roma, Italye-mail: milena.benedettini@inaf.it
2
Center for Interdisciplinary Exploration and Research in Astrophysics and Department of Physics and Astronomy,
Northwestern University, Evanston, IL 60208, USA
3
INAF – Osservatorio Astrofisico di Arcetri, Largo E. Fermi 5, 50125 Firenze, Italy
4
INAF – Istituto di Radioastronomia & Italian ALMA Regional Centre, Via P. Gobetti 101, 40129 Bologna, Italy
5
Universidade de São Paulo, IAG Rua do Matão, 1226, Cidade Universitária, 05508-090 São Paulo, Brazil
6
ESO/European Southern Observatory, Karl-Schwarzschild-Strasse 2, 85748 Garching bei München, Germany
Received 13 June 2019 / 22 November 2019
ABSTRACT
Aims. We present the Forgotten Quadrant Survey (FQS), an ESO large project that used the 12 m antenna of the Arizona Radio
Observatory to map the Galactic plane in the range 220◦ < l < 240◦ and −2.◦ 5 < b < 0◦ , both in 12 CO (1–0) and 13 CO (1–0), at aspectral resolution of 0.65 km s−1 and 0.26 km s−1 .
Methods. We used the (1–0) transition of carbon monoxide to trace the molecular component of the interstellar medium. Our data set
allows us to easily identify how the molecular dense gas is organised at different spatial scales: from the giant clouds with their denser
filamentary networks, down to the clumps and cores that host the new-born stars and to obtain reliable estimates of their key physicalparameters such as size and mass.
Results. We present the first release of the data of the FQS survey and discuss their quality. Spectra with 0.65 km s−1 velocity channels have noise ranging from 0.8 K to 1.3 K for 12 CO (1–0) and from 0.3 K to 0.6 K for 13 CO (1–0). In this first paper, we used the12
CO (1–0) spectral cubes to produce a catalogue of 263 molecular clouds. The clouds are grouped in three main structures corresponding to the Local, Perseus, and Outer arms up to a distance of ∼8.6 kpc from the Sun. This is the first self-consistent statisticalcatalogue of molecular clouds of the outer Galaxy obtained with a subarcminute spatial resolution and therefore able to detect not only
the classical giant molecular clouds, but also the small clouds and to resolve the cloud structure at the sub-parsec scale up to a distanceof a few kiloparsec. We found two classes of objects: structures with sizes above a few parsecs that are typical molecular clouds andmay be self-gravitating, and subparsec structures that cannot be in gravitational equilibrium and are likely transient or confined byexternal pressure. We used the ratio between the Herschel H2 column density and the integrated intensity of the CO lines to calculate
the CO conversion factor and we found mean values of (3.3 ± 1.4) × 1020 cm−2 (K km s−1 )−1 and (1.2 ± 0.4) × 1021 cm−2 (K km s−1 )−1 ,for 12 CO (1–0) and 13 CO (1–0), respectively.
Conclusions. The FQS contributes to the general effort in producing a new generation of high-quality spectroscopic data for the
Galactic plane in the less-studied third Galactic quadrant toward the outer Galaxy. The FQS has produced a data set of great legacy
value, largely improving the data quality both in terms of sensitivity and spatial resolution over previous data sets.
Key words. ISM: clouds – ISM: structure – ISM: kinematics and dynamics

1. Introduction
To reconstruct the distribution of matter in our Galaxy and tounderstand how the diffuse interstellar medium (ISM) is structured are some of the main objectives in modern astronomy.
Large surveys at all wavelengths have been carried out to map
the diffuse gas content of our Galaxy as well as the densest
condensations from which new stars will be born. In particular,
over recent decades many projects have surveyed the Milky Way
plane at several millimetre and submillimetre wavelengths with?

Full Table A.1 is only available at the CDS via anonymous ftp
to cdsarc.u-strasbg.fr (130.79.128.5) or via http://cdsarc.
u-strasbg.fr/viz-bin/cat/J/A+A/633/A147

the aim of deriving the properties of the ISM through molecular
spectroscopy and thermal continuum emission.
Surveys of the molecular component of the ISM play anessential role in our understanding of where and how starformation takes place in our Galaxy. The lower-frequency rotational transitions of CO are readily observed even in quite tenuous molecular gas, and the lowest of these, the (1–0) line at
115 GHz, is the preferred tracer of the cold molecular componentof the ISM. The first CO surveys were carried out in the seventies, covering different parts of the Galactic plane. The first complete survey of the Galactic plane in CO (1–0) was compiled by
Dame et al. (1987) collecting previous partial surveys. Anotherextended survey covering the inner Galaxy and part of the third
quadrant is the NANTEN survey (Mizuno & Fukui 2004).

Article published by EDP Sciences

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More recently, infrared and submillimetre Galactic plane
surveys were carried out both from space, such as the Galactic plane Infrared Mapping Survey Extraordinaire (GLIMPSE,
Benjamin et al. 2003), the MIPS Galactic plane survey (MIPSGAL, Carey et al. 2009), the Herschel InfraRed Galactic plane
survey (Hi-GAL, Molinari et al. 2010), and the all-sky survey
from WISE (Wright et al. 2010), and from ground, such as the
Bolocam Galactic Plane Survey (BGPS, Aguirre et al. 2011), the
APEX Telescope Large Area Survey of the Galaxy (ATLASGAL, Schuller et al. 2009), and the JCMT Plane Survey (JPS,
Moore et al. 2015). With their good sensitivity and spatial resolution these surveys started to provide a picture of the distribution of dust at all scales: from diffuse clouds and filamentary
structures down to pre-stellar clumps and young stellar objects(YSOs). These multi-wavelength continuum data are ideal toderive the dust temperature of the sources, but in order to obtain
the other relevant physical properties, such as the mass, andestablish whether they are in gravitational equilibrium, a good
estimate of the source distance is required. The simplest way tomeasure distances in our Galaxy is to measure the vLSR of gas
from spectroscopic observations and then derive the distance byassuming a Galactic rotation model. Furthermore, the identification of possible multiple molecular structures of the ISM at
different distances along the same line of sight and the characteristics of their dynamical state can only be derived from spectroscopic observations.
The first generation of CO surveys are at very poor spatial resolution (a few arcminutes) and coarse spatial sampling,making it very difficult to use them in conjunction with themost recent continuum surveys with spatial resolution froma few to tens of arcseconds. Therefore, the production of a
new generation of unbiased surveys of the molecular gas withresolution and sensitivity comparable to those of the continuum surveys has become mandatory. Several projects have been
started in recent years to map large portions of the Milky
Way in CO and its isotopologues, at high spatial (sub-arcmin)and spectral (a few tenths of a km s−1 ) resolution. The largest
spectroscopic surveys are: the Galactic Ring Survey (GRS,
Jackson et al. 2006), the Mopra Southern Galactic Plane CO
survey (Burton et al. 2013), the Exeter-FCRAO CO Galactic
Plane Survey (Mottram & Brunt 2010), the Three-mm Ultimate
Mopra Milky Way Survey (Barnes et al. 2015), the Structure,
Excitation, and Dynamics of the Inner Galactic Inter-Stellar
Medium (SEDIGISM) survey (Schuller et al. 2017), The Milky
Way Image Scroll Painting (Jiang & Li 2013), and the FOREST
Unbiased Galactic plane Imaging survey with the Nobeyama
45-m telescope (FUGIN, Umemoto et al. 2017). The third
Galactic quadrant is the least observed, with several piecesof the Galactic plane not yet covered by ongoing projects(Heyer & Dame 2015). The Forgotten Quadrant Survey (FQS)
aims at covering this gap by mapping a strip in the range 220◦ <l < 240◦ and −2◦.5 < b < 0◦ , centred around a midplane that
follows the Galactic warp, both in 12 CO and 13 CO (1–0) transitions. The longitude range covers the outer Galaxy, crossing
the Local arm, the Perseus arm, and the Outer arm, a region
still densely populated with a variety of star-forming regions
and complex networks of filamentary structures (Elia et al.
2013).
The FQS is an ESO project awarded 700 h of observing timeat the Arizona Radio Observatory (ARO) 12 m antenna. The primary aim of the project is the characterisation of the molecular gas in this poorly studied portion of the third quadrant of
the Milky Way. To achieve this goal we will produce catalogues
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of the main structures identified in the FQS spectroscopic data
at different spatial scales, from large molecular clouds to dense
cores and we will determine their distance and derive their main
physical properties, such as size and mass. In particular, the mainscientific goals of the survey are the following.
i. To produce a catalogue of molecular clouds (MCs) in orderto get a better understanding of the large-scale structure in thisportion of the outer Galaxy, with improved spatial resolution.
The FQS data allow the identification of MCs, disentangling
clouds at different distances along the same line of sight. The
simultaneous observation of the 12 CO and 13 CO isotopologues
allows the computation of a reliable estimate of the CO column
density and mass for each cloud.
ii. To produce a catalogue of filamentary structures and toinvestigate their formation mechanism. Herschel photometricsurveys have shown that a large part of the ISM is structured
in filaments and a catalogue of filament candidates of the Galactic plane from the Herschel data was published (Schisano et al.
2020). The FQS spectroscopic data allow us to verify the kinematic coherency of the filament candidates identified in the HiGAL photometric maps. For these filaments the velocity field
can be measured, allowing the study of their internal dynamics
and feeding mechanisms.
iii. To produce a catalogue of dense cores and build the coremass function (CMF). The spectral resolution of the FQS dataoffers the possibility to measure the non-thermal contribution to
the line broadening and thus to measure the virial mass of dense
cores and clumps. The comparison of the virial mass with thecore or clump mass derived from submillimetre continuum surveys (e.g. the Hi-GAL catalogue) determines whether they are
gravitationally bound or unbound. This is of utmost importancefor distinguishing structures that may eventually form a star ora multiple stellar system from those that will disperse, and thus
for constructing a correct CMF.
iv. To derive distance estimates for the dense compact
sources detected in the continuum far-infrared maps. The correct exploitation of catalogues of compact and potentially starforming clumps or cores produced by IR and submillimetre continuum surveys (e.g. the Hi-GAL catalogue) is based on the possibility to derive a reliable mass estimate, which in turn depends
on a correct distance measurement. The FQS data provide thevLSR of the emitting gas from which its distance can be derived
by assuming a Galactic rotation model. The comparable spatial
resolution between FQS and Hi-GAL data allows one to easily
associate this distance to the Hi-GAL sources.
All these scientific topics will be investigated in several dedicated papers. In this first paper we present the FQS project withits data products and we address the first item of our listed specific goals. Indeed, we present the first self-consistent catalogueof molecular clouds of the outer Galaxy extracted from fully
sampled 12 CO (1–0) maps at subarcminute spatial resolution.
The structure of the paper is as follows. Section 2 describes theobservational setup and the data-acquisition strategy. Section 3
presents our data-reduction pipeline with the final products andthe quality assessment. In Sect. 4 we describe the 12 CO (1–0)
and 13 CO (1–0) line emission maps. The MCs catalogue is presented in Sect. 5 with the extraction algorithm and the derived
physical properties. In Sect. 6 we focus on the nature of the
subparsec-scale structures and in Sect. 7 we show how the MCs
are distributed in the plane of the Milky Way. Section 8 contains
a discussion of the CO-to-H2 conversion factor and conclusions
are drawn in Sect. 9.

M. Benedettini et al.: The Forgotten Quadrant Survey

Fig. 1. Coverage of the FQS survey with the observed 300 × 300 tiles (squares) overplotted on the H2 column density maps derived from Herscheldata. Top panel: range of Galactic longitude 219◦ –230◦ , the coverage of 12 CO (1–0) and 13 CO (1–0) data (black squares) is the same. Bottom panel:
range 230◦ –240◦ , where the coverage of the 13 CO (1–0) data (yellow squares) is smaller than the coverage of the 12 CO (1–0) data (composed byyellow squares plus black squares). The intensity bar of the H2 column density at the bottom is in units of cm−2 . The two contours correspondto N(H2 ) = 2.8 × 1021 cm−2 and 3.8 × 1021 cm−2 , equivalent to a visual extinction level of 3 and 4 mag, respectively. The sensitivity level of ourobservations allows detection of signal only from regions inside the contour at about 3 mag for 12 CO (1–0) and at about 4 mag for 13 CO (1–0).

2. Observations
2.1. Observational setup

We used the ARO AEM ALMA prototype 12 m antenna with
the ALMA Type Band-3 Receiver to map the Milky Way planein the range 220◦ < l < 240◦ and −2◦.5 < b < 0◦ , following the
Galactic warp, both in 12 CO (1–0) and 13 CO (1–0). Observations
started in November 2016 and ended in June 2018.
The survey area was divided into partially overlapping tilesof 300 × 300 size, with sides aligned along the Galactic longitude
and latitude (see Fig. 1). Each tile was observed twice in the OnThe-Fly observing mode in mutually orthogonal scan directions,
one along l and one along b. The data dumping time is 0.1 s.
The distance of the scanning rows is 1800 and the scanning speed
is 7500 /s, resulting in a better-than Nyquist spatial sampling of
the telescope beam, that is 5500 at 115 GHz. Several positions for
the OFF measurement were used, so that for each tile the distance between the centre of the tile and the OFF position was
smaller than 2◦ . All the OFF positions were checked to be freeof 12 CO (1–0) emission.
The receiver was tuned to 115.271 GHz and 110.201 GHz
for 12 CO (1–0) and 13 CO (1–0), respectively. The back-end
was composed of a 256-channel filter bank at 250 kHz spectral
resolution (hereafter FB250), corresponding to a total velocity
coverage of 166 km s−1 and a velocity resolution of 0.65 km s−1 ,in parallel with a second 256-channel filter bank at 100 kHz

spectral resolution (hereafter FB100), corresponding to a total
velocity coverage of 66 km s−1 and a velocity resolution of
0.26 km s−1 . Antenna temperature is converted to main beam
temperature by dividing by the beam efficiency of 0.80.
2.2. Calibration accuracy and stability

In order to evaluate the temperature calibration accuracy and tomonitor the stability of the system over the long period of dataacquisition, at the beginning of each observing day we acquired
the spectrum of a reference source. We observed IRAS 07046–
1115 at RA(J2000) = 7h 07m 01.6s and DEC(J2000) =−11◦ 200 1700 . This source was previously observed in 12 CO(1–0) at SEST by Wouterloot & Brand (1989). The total integrated intensity of the 12 CO (1–0) line towards IRAS 07046–
1115 measured duringR the observation campaign with FB250
has a mean value of T A∗ dv = (71.5 ± 1.9) K km s−1 ; for the
spectra of FB100 the mean value is (70.8 ± 1.9) K km s−1 . The
two measurements are in agreement within the uncertainties. We
do not observe any systematic trend in time of the integrated
intensity measurements and the deviations of the measured integrated intensity with respect to its mean value are within ∼5%,for both filter banks, smaller than the absolute temperature calibration accuracy of the ARO 12 m data which is ∼10%.
In order to crosscheck the absolute temperature calibrationof the ARO 12 m data, we compared the ARO measurement with
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level of our observations, the 12 CO (1–0) line was detected only
in regions with H2 column densities above ∼2.8 × 1021 cm−2 ,equivalent to a visual extinction level of AV ≥ 3 mag, assuming the conversion factor N(H2 )/AV = 9.4 × 1020 cm−2 mag−1(Bohlin et al. 1978). For the 13 CO (1–0) line a similar level was
found of ∼4 mag. We therefore used the 3 mag and 4 mag contour level of the Herschel H2 column density map as a criterion for identifying the regions to be observed in 12 CO (1–0) and13
CO (1–0), respectively. In the area not covered by the ARO
observations we are confident that no significant CO emission
is present above the sensitivity level of our survey. This statement has been verified a posteriori, as discussed in Sect. 4. In
Fig. 1 we show the total coverage in 12 CO (1–0) and in 13 CO (1–0) overplotted on the Herschel H2 column density map.
Fig. 2. Histogram of the normalised relative difference between themain beam temperature of the 12 CO (1–0) line measured in the FQS andin the FUGIN survey. The data are resampled to a common spatial resolution (5500 ), pixel size (171700. 3), and velocity channel (1 km s−1 ). All
voxels (l, b, v) with emission above 5σ for both sets of data are considered.The continuum line shows the best-fit Gaussian distribution withpeak at 0.01 and standard deviation of 0.23.

the same measurement of the 12 CO (1–0) line taken with othertelescopes. Firstly, we used the observation made with SEST
towards IRAS 07046–1115. Converting the antenna temperature
to main beam temperature by assuming a beam efficiency of
0.80
results in an integrated intensity measured at ARO
R
R 12 m of
T MB dv = (89 ± 2.4) K km s−1 . The SEST data give T MB dv =93.6 K km s−1 , a slightly higher value that could, at least partially, be ascribed to the different beam of the two telescopes,
namely 5500 for ARO 12 m and 4300 for SEST and that, in any
case, is within the absolute calibration accuracy. An additional
comparison was done with the FUGIN survey (Umemoto et al.
2017), that partially overlaps with FQS in the range 220◦ < l <236◦ , −1◦ < b < 0◦ . To this aim, we considered the common
area of one square degree at 225◦ < l < 226◦ and −1◦ < b < 0◦ .
The Nobeyama 45 m Fugin 12 CO (1–0) data were convolved tothe beam size of the ARO 12 m, rebinned to a common velocity
channel of 1 km s−1 and regridded to the FQS spatial grid. Then,for each voxel (l, b, v) with T MB of the 12 CO (1–0) line above
5σ for both sets of data, we calculated the relative main beam
temperature difference, [T MB (FUGIN) − TMB (FQS)]/TMB (FQS),
whose histogram is shown in Fig. 2. The distribution is wellfitted by a Gaussian function with peak at 0.01 and standard
deviation of 0.23. The tail at the highest relative difference,
namely the voxels with relative difference ≥0.6, is a statistical effect associated to voxels with the lowest signal-to-noise
ratios and they are only 4% of the total. In conclusion, there
is no systematic difference between the two sets of data and in
about 68% of the voxles the FQS and FUGIN data are consistent
within 23%, in agreement with the intensity variations measured
towards the reference source by FQS (5%) and FUGIN (20%,
Umemoto et al. 2017).
2.3. Mapped area

The selected portion of the Galactic plane, namely 220◦ < l <240◦ and −2◦.5 < b < 0◦ , was not fully covered by our ARO
observations. In fact, we used the cold dust emission detectedin the Herschel maps as a proxy of the CO emission. We compared the 12 CO (1–0) integrated line maps in an initial set oftiles with the H2 column density maps derived from Herscheldata (Schisano et al. 2020) and found that, given the sensitivity
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3. Data reduction
3.1. Data reduction pipeline

Data reduction was carried out with a dedicated pipeline developed with the GILDAS package1 , which requires minimal
human intervention. After manually flagging spectra affected byobvious artefacts, the data were automatically processed tile bytile. For each tile, all the spectra were averaged to obtain a lownoise spectrum, which was used to identify the velocity intervals
where emission is detected. The remaining channels were used
to fit a first-order polynomial to the baseline, spectrum by spectrum, to obtain baseline-subtracted data. For a given molecularline and back-end, we merged all the spectra after resamplingthem both in space and velocity through standard commands in
GILDAS to create spectral cubes. In this way, for both 12 CO(1–0) and 13 CO (1–0) data, we obtained two cubes with different
spectral resolutions: one with channels of 0.3 km s−1 , from the
FB100 back-end, and the other with 1 km s−1 channels, from the
FB250 back-end, slightly degrading the native spectral resolution of the spectra in order to increase the signal-to-noise ratio.
The spatial pixel size for both cubes is 1700. 3. To produce a manageable set of maps, we divided the survey area into two fields,
one covering the Galactic longitude range 220◦ < l < 226◦.6 andthe other covering the range 231◦ < l < 239◦.7, and we createdseparate spectral cubes for the two fields.
Calibrated single spectra and spectral cubes are publicly
available in the FQS web page2 . Calibrated spectra for each300 × 300 tile are packed into eight files in the GILDAS format, distinguishing between observed line (12 CO (1–0) or 13 CO(1–0)), scan direction (along l or b), and filter banks (FB250
and FB100). The FQS spectral cubes are made available tothe community also in the framework of the VIALACTEA3
knowledge base (Molinaro et al. 2016), accessible through the
VIALACTEA visual analytics tool (Vitello et al. 2018), whichallows the inspection of our data in combination with other continuum and spectroscopic data of the Galactic plane from many
surveys from infrared to radio frequencies.
3.2. Data quality

A data quality assessment was performed on the acquired datausing the spectra of FB250. We spatially resampled the spectraof the 300 × 300 tile acquired in a single scan direction in spatial pixels of 1700. 3, equivalent to about one-third of the beam.
1

The GILDAS software has been developed at IRAM and Observatoire de Grenoble: http://www.iram.fr/IRAMFR/GILDAS
2
http://fqs.iaps.inaf.it
3
http://vialactea.iaps.inaf.it

M. Benedettini et al.: The Forgotten Quadrant Survey

Fig. 3. Median values of rms vs. T sys of the single tiles observed with
FB250 in each scan direction separately. Spectra are calibrated in main
beam temperature and have a velocity channel of 0.65 km s−1 . Triangles
are for 12 CO (1–0) and squares for 13 CO (1–0).

Fig. 4. Histograms of rms of the spectra observed in FB250 in eachspatial pixel of the final spectral cubes resampled in 1 km s−1 velocity
channels. Blue is for 13 CO (1–0) and black for 12 CO (1–0).

Fig. 5. Galactic longitude–velocity diagrams of the 12 CO (1–0) line for the two ranges of Galactic longitude 220◦ –226◦.6 (top panel) and 231◦ –239◦.7 (bottom panel).

For each of these tiles we calculated the median value ofthe system temperature (T sys ) and the root mean square (rms)noise in units of main beam brightness temperature, in a reference velocity channel, at the native spectral resolution of
0.65 km s−1 . Maps acquired during unfavourable weather conditions were identified as outliers with respect to the average
distributions. In order to guarantee an acceptable homogeneity
in survey sensitivity we discarded the maps of the noisy tiles
and re-observed those tiles. In Fig. 3 we show the median valueof the rms, in T MB , of the single tiles observed in one scan

direction at the spectral resolution of 0.65 km s−1 plotted as afunction of the median value of the corresponding T sys for allof the good tiles. The rms ranges from 0.79 K to 1.31 K for
12
CO (1–0) and from 0.33 K to 0.58 K for 13 CO (1–0). We notethat the rms of our final spectral cubes is lower since each spatial
pixel is observed at least twice and the data were smoothed to aspectral resolution of 1 km s−1 . The histogram of the rms measured in each spatial pixel in the final cubes for both 12 CO (1–0)
and 13 CO (1–0) data is shown in Fig. 4. The median value of therms in the spectral cubes of the 12 CO (1–0) is 0.53 K and theA147, page 5 of 16

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Fig. 6. 12 CO (1–0) intensity maps of the l = 220◦ –226◦.6 field, integrated over velocity ranges 5–25 km s−1 (top panel), 30–45 km s−1 (central
panel), and 50–75 km s−1 (bottom panel). First contour level is at 5 K km s−1 and steps are of 20 K km s−1 .

Fig. 7. 12 CO (1–0) intensity maps of the l = 231◦ –239◦.7 field, integrated over velocity ranges 13–34 km s−1 (top panel), 34–60 km s−1 (central
panel), and 70–87 km s−1 (bottom panel). First contour level is at 5 K km s−1 and steps are of 20 K km s−1 .

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M. Benedettini et al.: The Forgotten Quadrant Survey

Fig. 8. 13 CO (1–0) intensity maps of the l = 220◦ –226◦.6 field, integrated over velocity ranges 5–25 km s−1 (top panel), 30–45 km s−1 (central
panel), and 50–75 km s−1 (bottom panel). First contour level is at 2 K km s−1 and steps are of 5 K km s−1 .

Fig. 9. 13 CO (1–0) intensity maps of the l = 231◦ –239◦.7 field, integrated over velocity ranges 13–34 km s−1 (top panel), 34–60 km s−1 (central
panel), and 70–87 km s−1 (bottom panel). First contour level is at 2 K km s−1 and steps are of 5 K km s−1 .

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Fig. 10. H2 column density maps (grey scale and black contour) derived from Herschel data with the 12 CO (1–0) intensity maps overplotted as
green contours. Top panel: range of Galactic longitude 219◦ –230◦ ; the 12 CO (1–0) emission is integrated over the velocity range 5–75 km s−1 ; green
contours are at 5, 25 and 50 K km s−1 . Bottom panel: range 230◦ –240◦ ; the 12 CO (1–0) emission is integrated over the velocity range 13–87 km s−1 ;green contours are at 5, 25, and 50 K km s−1 . The intensity bar of the H2 column density at the bottom is in units of cm−2 . The black contour
corresponds to N(H2 ) = 2.8 × 1021 cm−2 , equivalent to a visual extinction level AV = 3 mag.

median rms for the cubes of the 13 CO (1–0) is 0.22 K. Comparing
our sensitivity level to that of the FUGIN survey (Umemoto et al.
2017), the other existing CO survey that partially overlaps with
FQS, we find that our sensitivity level is about a factor of 0.4
better.

4. 12 CO and 13 CO (1–0) emission maps
In Fig. 5 we present the velocity vs. Galactic longitude plotof the 12 CO (1–0) line, obtained by integrating the emission
over the observed range of Galactic latitude (−2◦.5 < b < 0◦ ).
Three main structures are clearly visible as well-separated, semicontinuous horizontal features, each of which contains molecular gas with similar velocity. These features correspond to three
spiral arms of the Galaxy: the first, with velocities between∼5 km s−1 and ∼30 km s−1 , corresponds to the Local arm, thesecond, with velocities between ∼30 km s−1 and ∼60 km s−1 ,corresponds to the Perseus arm, and the third, with velocities
between ∼60 km s−1 and ∼90 km s−1 , corresponds to the Outerarm (see Sect. 7 for further details). In Figs. 6 and 7 we plot
the intensity maps of the 12 CO (1–0) line, integrated over thethree velocity ranges. These show the spatial distribution of themolecular gas in the observed portion of the Galactic plane. In
several parts of the plane, multiple clouds are present along thesame line of sight, but they are easily disentangled in the spectra because their velocities are well separated (see Fig. 5). The
intensity maps of 13 CO (1–0) integrated over the same velocity ranges as for 12 CO (1–0) are presented in Figs. 8 and 9.
Although the sensitivity level of our 13 CO (1–0) maps is about
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a factor of two better than that of the 12 CO (1–0) maps, the 13 CO(1–0) emission traces only the central, densest part of themolecular gas, because of the higher critical density and thelower chemical abundance of 13 CO. In Fig. 10 we compare the
Herschel H2 column density map with the 12 CO (1–0) integrated
intensity map. The H2 column density contour of 2.8 × 1021 cm−2that we used as proxy for the 12 CO (1–0) line emission matches
well with the 3σ level of the CO map, except a few regions where
it encompasses a slightly more extended area with respect tothat emitting in CO. Therefore, the coverage of our CO survey,
although patchy, catches all the emission of the observed lines
above the sensitivity level of our observations in the Galactic
plane in the range 220◦ < l < 240◦ and −2◦.5 < b < 0◦ , with thevery limited exception of a handful of small and isolated spots.
We note that several structures seem to extend beyond thecoverage of our survey and therefore they may not be fully
mapped in our survey, in particular at (l ∼ 222◦.3, b < −2◦.5);(l ∼ 224◦.5, b < −2◦.5); (l ∼ 232◦ , b < −2◦.5); (l ∼ 232◦.35,b > 0◦ ); (l ∼ 234◦ , b > 0◦ ), and (l ∼ 237◦ , b < −2◦.5).

5. Molecular cloud catalogue
5.1. Cloud identifications

To reconstruct the large-scale distribution of the molecular gas
in the survey area, we produced a catalogue of molecular clouds
using the Spectral Clustering for Interstellar Molecular Emission
Segmentation (SCIMES) algorithm (Colombo et al. 2015). The
algorithm is based on a cluster analysis of dendrograms of 3D

M. Benedettini et al.: The Forgotten Quadrant Survey(l–b–v) data cubes that performs particularly well for the identification of large structures such as the MCs (e.g. Colombo et al.
2015; Schuller et al. 2017). SCIMES considers the dendrogram
tree of the 3D structures in the data cube (using the implementation of Rosolowsky et al. (2008) for astronomical data sets)
and groups different leaves together into “clusters” of leaves
based on one or more criteria. The algorithm requires the definition of some parameters that regulate the level of clustering. We
tuned these values to maximise the connection of the identified
structures in large, kinematically coherent clouds (see below for
the numerical value of the parameters).
We performed the identification of the MCs using the12
CO (1–0) line, since it is the brightest line, and the spectral
cubes from the filter bank with the largest velocity coverage,
namely the FB250. As a first step we masked the FB250 spectral cubes using the dilate mask approach (Rosolowsky & Leroy2006). In practice, we selected pixels in which the signal is
above 5 × rms in two consecutive velocity channels and further
extended the mask to include all adjacent pixels in which thesignal is above 2 × rms. We then produced the dendrogram treeof the masked cube without any further filtering of pixels basedon signal-to-noise ratio (S/N). This ensures the inclusion of the
low-level borders of bright peaks and maximises the connections
between structures composed of high-intensity peaks connectedby lower intensity levels, while excluding all the pixels withsignal below 2 × rms and those that are not connected to highintensity peaks. In practice, we selected structures with S /N > 5
in at least one spatial pixel and extended their borders up to pixels with S/N = 2. To produce the dendrogram tree of the masked
cube, we set the minimum difference between two peaks forthese to be considered as separate leaves (min_delta) to 7 × rms
and the minimum number of pixels of the spectral cube needed
for a leaf to be independent to min_npix = 3πHPBW2 /(4ln2).
We finally applied the spectral clustering using the volume of
the isosurface in the (l–b–v) space as a criterion. Since we werealso interested in clouds that may simply have little substructure
within them, we also included the single leaves of the dendrogram which had been excluded by the clustering algorithm. To beconservative, we selected only single leaves with an angular arealarger than two times the beam area, that is, the spatially wellresolved structures. This introduces a minimum physical area ofthe structures of our catalogue that depends on their distance (seeFig. 11), ranging from 0.16 pc2 at 1 kpc to 10.31 pc2 at 8 kpc.
In total, we identified 263 MCs in our 12 CO (1–0) data cube.
A view of the position and contours of the identified structures
can be seen in Fig. 12. For each cloud we measured the following properties, using the definition in Rosolowsky & Leroy(2006): centroid position in Galactic coordinates, semi-major
and semi-minor axes, estimated as the rms of the intensityweighted second moments along the direction of maximum
cloud extent and its perpendicular direction, respectively,
position angle, intensity-weighted velocity, and relative velocity dispersion (σv ). These properties are listed in Table A.1 fora subsample of MCs. The complete catalogue can be found in
the online material. In the catalogue we also give the numberof leaves, identified by the dendrogram, that compose the cloud;235 molecular clouds are composed of only one leaf, indicatingthat they are relatively homogeneous structures.
5.2. Cloud physical parameters

From the central velocity of the 12 CO (1–0) line we derived
the kinematic distance of the emitting cloud by applying a
Galaxy-rotation model. We used the IDL routine of the CPROPS

Fig. 11. Area of the MCs identified in our survey vs. their distance. The
continuum line defines the limit in the cloud area of two times the beam
area; this limit was used as a selection criterion. The possible presenceof clouds below this line cannot be established by our survey.

package (Rosolowsky & Leroy 2006) for the distance estimate.
This assumes a flat Galactic rotation curve, which is a good
approximation for the outer Galaxy, with a solar galactocentric distance of R0 = 8.34 kpc and a rotation velocity of Θ0 =240 km s−1 (Reid et al. 2014). Since we are looking towards theouter Galaxy, our distance estimate is not affected by the near or
far ambiguity.
From the geometrical mean of the semi-axis (σr ) we derived
the radius of the equivalent spherical cloud: R = 1.91σr(Rosolowsky & Leroy 2006). The mass of the clouds was calculated fromZ
M = µH2 mH NH2 dA,(1)
where µH2 = 2.8 is the molecular weight for the hydrogenmolecule that takes into account the presence of helium, mH is
the mass of the hydrogen atom, A is the area of the cloud, andNH2 is the molecular hydrogen column density. We have threedifferent possibilities to derive NH2 and therefore the mass ofthe clouds. The classical method to derive the H2 column density from the 12 CO (1–0) total integrated intensity is to assume aconstant conversion factor between the two:NH2 = XCO ICO .

(2)

However, the most recent CO observations of MCs withimproved spatial resolution of the order of tens of arcseconds
and therefore able to resolve the cloud internal structure, atleast for the nearest clouds, have shown that assuming a constant XCO is too simplistic an approach. Indeed, the XCO factor, measured over tens of square degrees in the Galactic plane,shows pixel by pixel variation of more than two orders of magnitude (e.g. Barnes et al. 2015; Schuller et al. 2017) due to the
combined effect of the change of excitation temperature, opticaldepth, and CO abundance, in regions with very different physical conditions, even within the same molecular cloud. We therefore preferred to derive the cloud mass from the H2 column density estimated by Herschel by using the far-infrared emission of
the cold dust and assuming a gas-to-dust mass ratio. In Sect. 8
we further discuss the reasons for this decision. The HerschelH2 column density map (Schisano et al. 2020) was derived byfitting the dust emission spectral energy distribution in four farinfrared wavelengths from 160 µm to 500 µm, under the assumption of a dust opacity law of κλ = κ300 (λ/300 µm)−β with
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A&A 633, A147 (2020)

Fig. 12. Border of the MCs of the FQS catalogue is drawn over the integrated intensity map of 12 CO (1–0).

κ300 = 0.1 cm2 g−1 (already accounting for a gas-to-dust ratioof 100) and a grain emissivity parameter β = 2 (Hildebrand1983). Being derived from photometric data, the Herschel H2column density map cannot disentangle clouds at different distances that overlap along the line of sight. We resolved this by
distributing the total H2 column density of the Herschel map
among all the velocity components identified in the same spatial pixels of the 12 CO (1–0) cube, when present, proportionally to their 12 CO (1–0) intensity. This is a correct assumption in
the low-density regions of the molecular clouds where the 12 CO(1–0) line can be optically thin. On the other hand, in the denser
regions of the clouds, the 12 CO (1–0) line is expected to be
optically thick and the former assumption introduces an uncertainty in the mass estimate. We stress that since the spatial pixels with a multiple velocity component represent a small percentage of the total survey coverage, 60% of the MCs of ourcatalogue are not affected by this problem at all. For the restof the sample we evaluated this uncertainty by performing thefollowing experiment. We derived an upper limit for the massof the clouds using the same Herschel H2 column density map,
but in the spatial pixels where multiple velocity components arepresent, instead of distributing the H2 column density of the
Herschel map among the components, we attributed all the H2column density of that pixel to each of the clouds at the different velocity. We then compared the cloud mass with the upper
limit and found that only 11% of the MCs of our catalogue
have a mass that could be underestimated by more than a factor of 0.5, and that never is the mass underestimated by more
A147, page 10 of 16

than a factor of two. The third possibility to estimate H2 column density is to calculate the CO column density and multiply
it by the CO chemical abundance with respect to H2 . Having observed the same transition of two isotopologues of carbon monoxide, we can derive an estimate of the CO column
density that takes into account the effect of the line opacity.
However, this exercise is deferred to a forthcoming paper.
We used the mass derived from the Herschel H2 column density map to calculate the average mass surface density (Σ) of theclouds as the ratio between the mass2 and the area. We then com5σv R
puted the virial parameter, αvir = MG
, where σv is the gas velocity dispersion, R the spherical radius, M the cloud mass and G
the gravitational constant (McKee & Zweibel 1992). We derived
the velocity dispersion of the gas from the measured full width12
at half maximum (FWHM)√ of the CO (1–0) line through therelation σv = FWHM/ 8 ln(2). We stress that we accurately
measured the FWHM since the 12 CO (1–0) line is spectrally
resolved in our data. Moreover, since we are looking toward theouter Galaxy where cloud crowding is not an issue, the 12 CO(1–0) observed lines are well fitted with a single Gaussian profile and our measurement of the gas velocity dispersion of thesingle MCs is not affected by blending of emission of possible
multiple clouds along the same line of sight. It is worth notingthat the used relation between FWHM and velocity dispersion
is valid for optically thin Gaussian emission line. For an optically thick line, the measured FWHM overestimates the velocity
dispersion by a factor that increases with the opacity. In particular, the line broadening due to the opacity becomes larger than

M. Benedettini et al.: The Forgotten Quadrant Survey

Fig. 13. Histograms of the distributions of masses (top-left), area (top-centre), average mass surface densities (top-right), aspect ratios (middleleft), equivalent spherical radius (middle-centre),velocity dispersion (middle-right), and virial parameters (bottom-left) of the FQS catalogue ofmolecular clouds.

50% of the measured line broadening for τ > 10 (Hacar et al.
2016). Since the 12 CO (1–0) is typically optically thick in thedensest part of MCs, our estimate of the gas velocity dispersion
and, in turn, that of the virial parameter should be considered as
an upper limit.
Distance and distance-dependent parameters of the clouds,
namely radius in pc, area in pc2 , mass, average surface density, and virial parameter are reported in Table A.1. We note thatsome clouds identified in the 12 CO (1–0) map are only partiallymapped or are not mapped at all in the Herschel H2 column density map (see Fig. 1). The mass and the surface density derived
for these clouds are lower limits and the virial parameter is anupper limit; these clouds are flagged with flag1 = 1 in Table A.1.
Other clouds extend up to the border of the 12 CO (1–0) map andtherefore might not be completely mapped by our observations.
For these clouds, identified with flag2 = 2 in Table A.1, the measured parameters suffer by an uncertainty that depends on how
much CO emission was missed.
5.3. Global properties of the catalogue

In the portion of the Milky Way plane covered by oursurvey, catalogues of MCs were previously produced byMiville-Deschênes et al. (2017) and Rice et al. (2016) using thesurvey of Dame et al. (2001). Thanks to the improved sensitivity
and spatial resolution of the FQS survey, we were able to detectmany more structures with respect to these two previous catalogues. In particular, in the same surveyed portion of the Galac-

Table 1. Statistical physical properties of the MCs of the FQS catalogue.

Mass (M )Area (pc2 )Surface density (M pc−2 )Aspect ratio
Equivalent spherical radius (pc)
Velocity dispersion (km s−1 )Virial parameterMedian

Mode

759
9.6
65
1.8
1.8
0.9
2.51

760
11.3
56
1.3
0.6
0.7
1.06

tic plane we identified 263 MCs while Miville-Deschênes et al.
(2017) found 67 structures and Rice et al. (2016) found none.
Although limited to about 20◦ in Galactic longitude, the FQS
MC catalogue represents a good statistical sample of MCs in
the outer Galaxy, containing structures distributed in all thethree main outer spiral arms up to a distance of ∼8.6 kpc from theSun.
The distributions of several physical parameters of the MCsof our catalogue are shown in Fig. 13 and their statistical properties are summarised in Table 1. The distributions of area andmass of our sample span over about four orders of magnitude,
and when expressed in logarithmic scale they are relatively symmetric with respect to their median value of 9.6 pc2 for the areaand 759 M for the mass. Of course our sample is incompleteat the lowest areas and masses because of the impossibility to
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A&A 633, A147 (2020)

reveal small clouds at large distances. In particular, from Fig. 11
it is clear that in our catalogue we are able to recover structures with area above ∼10 pc2 over the entire distance range of
8 kpc. Despite the large range covered by both mass and area,
their ratio, that is the average surface density, is less variable,
spanning a narrower range from 20 to 120 M pc−2 , with a relatively symmetric distribution with respect to its median valueof 65 M pc−2 . This indicates a relatively narrow correlation
between the mass and physical dimensions of the clouds. Fitting
the area–mass relation with a linear relation in the log-log plane,from the 10 pc2 area limit we can estimate a corresponding limitof the cloud mass of 625 M . For both mass and area, these twolimits correspond to the turning points of the respective distributions, giving an indication of the completeness limits of the FQS
catalogue.
The distributions of aspect ratio, radius, velocity dispersion,and virial parameter are narrow and well peaked, with a little tailtoward the highest values. Their modes are: 0.6 pc for the radius,
1.3 for the aspect ratio, 0.7 km s−1 for the velocity dispersion,and 1 for the virial parameter.
Comparing the properties of our sample with othercatalogues extracted from the same tracer such as theMiville-Deschênes et al. (2017) and Rice et al. (2016) catalogues, we find that our structures are typically smaller andless massive. For example, for the subsample of the 67 cloudsof the Miville-Deschênes et al. (2017) catalogue with centroid
inside the FQS surveyed area, the median value of the radius is
26 pc while in our sample the median value is 1.8 pc and only
13 clouds have radii larger than 10 pc. This is a direct consequence of the improved spatial resolution of our survey thatis able to identify also parsec- and subparsec-size clouds thatare otherwise blended or undetected in the Dame et al. (2001)
0
data, acquired with a telescope beam of 8.4,
which is aboutnine times larger than our beam. This indicates that the newgeneration of CO surveys, at subarcminute spatial resolution,
can detect not only the classical large molecular clouds, thatis, molecular structures with radii above 10 pc, but also parsecand subparsec-sized clouds up to a distance of 2–3 kpc, whichmeans structures that were not detectable in the first generationof CO surveys. This is also confirmed by other MCs catalogues
based on subarcminute-spatial-resolution data, such as the GRS(Roman-Duval et al. 2010) and the SEDIGISM (Schuller et al.
2017) catalogues. Both these latter catalogues, even if covering
portions of the Galactic plane inside the solar circle and using
another tracer, the 13 CO (1–0) line, find a significant number ofmolecular clouds with radii below a few parsecs.
One might wonder whether or not the relatively low values ofthe measured radius of the clouds in our catalogue could be due
to an artificial segmentation of larger structures induced by ourcloud-identification method. We stress that we tuned the parameters which regulate the cloud identification algorithm in order tomaximise the level of clustering of adjacent structures. We also
did an a posteriori visual check of the results and we verified thatthe identified clouds were well-defined structures in our dataset, clearly separated both spatially and spectrally from possible
adjacent structures. In a few regions however, it is also possible that some small clouds that appear to be independent structures in our data set, but are spatially close to other structureswith similar velocity, could actually be part of a much larger
complex not properly traced in its fainter parts by the measured
12
CO (1–0) line, for sensitivity reasons. This could be the casefor the few-tenths-of-a-parsec-sized structures of the cataloguethat could represent the high-density clumps of a more extended
and less dense cloud. In Sect. 6 we further discuss this topic.
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Fig. 14. Radius–virial parameter plot of the MC catalogue. The clouds
that are associated with one or more protostar of the Herschel 70 µm
catalogue are enclosed in a green diamond.

As an additional check of the reliability of the small MCs,
we verified whether or not these clouds are associated to an
enhancement of the H2 column density. In particular, we used
the following procedure. For all the MCs with a circular radiusbelow 1 pc and fully mapped in the Herschel map, we selecteda region of 2 pixels in width along the border of the area of thecloud as defined in the CO data. We then calculated the medianvalue of the H2 column density in this border and considered thismedian value as a sort of H2 column density background level
for the cloud. Finally, we calculated the percentage of the areaof the cloud where the H2 column density is higher than the
background H2 column density. Values of this percentage above
the 50% indicate that the CO cloud is associated to a systematic enhancement of the H2 column density with respect to themedian value in its surrounding. We found that only 5 MCs of
the selected 51 small clouds have a percentage below 50% andthe median value of the percentage is 78%. This result reinforces
our conclusions that the small MCs are real overdensities against
the underlying diffuse emission.

6. Subparsec-sized structures
Figure 14 shows the virial parameter, estimated from the12
CO (1–0) line, as a function of cloud radius, coherent with
our definition of the cloud radius and mass. We note that sincethe 12 CO (1–0) line is typically optically thick in MCs, at leastin the denser regions of the clouds, the measured virial parameters are upper limits. In this plot we excluded clouds not fully
mapped in Herschel, for which we did not derive the total mass,and clouds that, touching the border of the 12 CO (1–0) map,
could be more extended. For these latter, both the measured
radius and mass must therefore be considered as lower limits. We note that the smallest clouds tend to have the largest
virial parameter. There is an anti-correlation between these twoquantities, with a Pearson’s coefficient of −0.7. For a Bonnor–
Ebert sphere in isothermal equilibrium and in absence of magnetic field, αvir & 2 is the critical value above which the cloudis gravitationally unbound and may expand and dissolve (e.g.
Kauffmann et al. 2013), unless confined by external pressure.
We found that all the clouds with R < 1 pc have a virial parameter above the critical value of two and that the largest values of

M. Benedettini et al.: The Forgotten Quadrant Survey

Fig. 15. Radius–velocity dispersion plot of the MC catalogue. The lines
represent the best fit of the relation σv = aRβ . For MCs with R < 2 pc
(dotted line) the best-fit coefficients are: a = −0.08 and β = 0.08. For
MCs with R ≥ 2 pc (solid line) the best-fit coefficients are: a = −0.32
and β = 0.59.

the virial parameter (αvir > 5) are associated only with clouds
with R < 2 pc. This seems to indicate that around the radius of
1–2 pc there could be a change in the properties of the clouds.
Structures with sizes above 1–2 pc are typical MCs that may be
self-gravitating while subparsec-sized structures cannot be kept
in by their self-gravity and would be transient or confined byexternal pressure.
To further explore the nature of these small structures, weinvestigated the presence of star formation activity inside themby looking for positional association with Herschel-detected protostars, namely very young protostellar objects still embeddedin their parental envelopes and therefore detectable in the catalogue of Herschel 70 µm, point sources. We found that none of
the clouds with αvir & 6 and R . 2 pc are associated with a
Herschel 70 µm point source. We deduced that the sample of small
clouds with high virial parameters is likely made up of transient
structures not able to activate the star formation process.
Another possible indication of the different nature of thesmallest clouds with respect to the largest ones is shownby their behaviour in the velocity dispersion vs. radius relation. This relation, known as the first Larson relation (Larson
1981), is expected to be of the form σv ∝ Rβ . The observed
non-thermal motions in MCs are always supersonic (thermal
motions for gas at T = 10 K is '0.1 km s−1 ), and the expectedvalue of β, if the origin of these motions is purely supersonic turbulence, the so-called Burger turbulence, is β = 0.5(McKee & Ostricker 2007). Various values of β in MCs havebeen derived by several authors, ranging from β ∼ 0.38 (Larson
1981) to β = 0.6 ± 0.3 (Miville-Deschênes et al. 2017). Restricting the evaluation of the relation to the interstellar clouds ofthe outer Galaxy, previous estimates give β = 0.47 ± 0.08
(Sodroski 1991), β = 0.53 ± 0.03 (Brand & Wouterloot 1995),
β = 0.45 ± 0.04 (May et al. 1997), and β = 0.53 ± 0.06 for
MCs in the third quadrant of the Galaxy (Rice et al. 2016),
all in agreement with the a Burger-like turbulence. However,the nature of the non-thermal motions in MCs is still debated(Krumholz et al. 2019), and it is not yet clear whether they
originate solely from turbulence or from large-scale gravitational collapse (Ballesteros-Paredes et al. 2011; Traficante et al.
2018a,b; Merello et al. 2019). We plot the σv –R relation forclouds of the FQS catalogue in Fig. 15. Two different regimes

appear for clouds with R ≥ 2 pc and clouds with R < 2 pc. For
MCs with R ≥ 2 pc the velocity dispersion increases with radiuswith an exponent β = 0.59, similar to what is expected frompure supersonic turbulence, and to that previously measured. On
the other hand, for MCs with R < 2 pc the relation is almost
flat, with β = 0.08. The non-thermal motions in the smallest
clouds seem to be independent of cloud size. These condensations, likely to be only transient structures, are probably notformed by the turbulent ISM, which would lead to a Burger-like
spectrum. It may be possible that these structures are the result ofa large-scale effect, such as spiral-arm density-wave shock that
injects the same amount of kinetic energy to all these objects.
This large-scale effect would dominate over the local turbulenceor the self-gravity within the smallest clouds, becoming increasingly irrelevant in the larger clouds.
As already noted in Sect. 5.3, it is also possible that some of
the really small structures, namely those with radii of only a few
tenths of a parsec, are part of a much bigger complex not properly traced by the 12 CO (1–0) line. If this is the case, the identified structures would be the densest clumps of a larger cloud.
We note that in a survey of massive clumps in the inner Galaxy(Traficante et al. 2018b) found that the Larson relations valid for
MCs are systematically violated. In particular, for these massive
clumps, with radii of 0.1 ≤ R ≤ 1 pc, comparable with the sizeof the small structures we are discussing here, Traficante et al.
(2018b) found β = 0.09 ± 0.04, similar to the value we found forclouds with R ≤ 2 pc. A more extended analysis of the dynamics
of these clouds in relation to their environment and a comparisonof the kinematics of these objects obtained from the 12 CO (1 − 0)
and 13 CO (1 − 0) lines will be the topic of a forthcoming paper.

7. Position in the Galaxy
We used the position and distance of the MCs in the FQS catalogue to describe the structure of the Milky Way in this poorly
known portion of the third quadrant. In Fig. 16 we show the
distribution of the clouds projected onto the Milky Way plane.
At any Galactic longitude the clouds are grouped in three wellseparated structures that trace the position of the spiral arms. The
dispersion of the positions in each structure is given not only by
the width of the arms but also by the uncertainty of the distancedetermination. Indeed, kinematic distances are affected by significant uncertainties due not only to the assumed Galactic rotation model but also to the unknown possible peculiar motions of
the clouds themselves. In Fig. 16 we compare the positions of theclouds derived from FQS data with the expected locations of the
spiral arms, adopting a log-periodic spiral form of the Galaxy(Reid et al. 2014). The pitch angle and the distance at a reference position of the three arms are taken from Xu et al. (2013)
for the Local arm, Choi et al. (2014) for the Perseus arm, andHachisuka et al. (2015) for the Outer arm. These authors used
the positions of water masers derived from parallax measurements as part of the Bar and Spiral Structure Legacy (BeSSeL)
survey to fit the parameters of the spiral arms. We found good
agreement between the position of the FQS clouds and the modelled position of the Local and the Perseus arms, while the modelled Outer arm lies further than the observed clouds. We notethat while the parameters of the Local and Perseus arms arederived by fitting about 20 water masers located in both the second and third quadrants, covering also the Galactic longitudeof the FQS survey, the parameters of the Outer arm are derived
from only 5 water masers, 4 of which are located in the second
quadrant. Therefore it is not surprising that they might not be
fully appropriate to describe the location of the Outer arm in theA147, page 13 of 16

A&A 633, A147 (2020)

Fig. 16. Face-on view of the portion of the Milky Way mapped in the
FQS survey where the origin of the (x, y) coordinates is the Galactic
centre. The orange point at (0,8.34) kpc marks the position of the Sun(Reid et al. 2014). Circles at 1, 4, and 8 kpc from the Sun are indicatedby dashed lines. The position of the MCs of the FQS catalogue are represented by squares. The modelled position of three spiral arms is traced
by yellow, green, and magenta lines for the Local, Perseus, and Outerarms, respectively. Model parameters for the spiral arms are derived
from the fitting of positions of water masers derived from parallax measurements as part of the BeSSeL survey (Xu et al. 2013 for the Localarm, Choi et al. 2014 for the Perseus arm, and Hachisuka et al. 2015
for the Outer arm). An alternative model of the Outer arm (Hou & Han
2014) is also shown as a magenta dashed line.

Fig. 17. Histogram of the XCO conversion factor derived from the ratioof the Herschel H2 column density and the line intensity of 12 CO (1–0) (continuum line) and 13 CO (1–0) (dotted line), pixel by pixel. The
vertical lines indicate the mean values of 3.3 × 1020 cm−2 (K km s−1 )−1
and 1.2 × 1021 cm−2 (K km s−1 )−1 .

longitude range of the FQS survey of 220◦ < l < 240◦ . In fact,
an alternative model of the Outer arm from Hou & Han (2014),
also shown in Fig. 16, seems in better agreement with the FQS
results.

8. X CO factor
We used the ratio between the Herschel H2 column density andthe integrated intensity of the CO lines to calculate the XCO conversion factor (Eq. (2)) for both 12 CO (1–0) and 13 CO (1–0). The
A147, page 14 of 16

histogram of XCO calculated in each spatial pixel of our maps isshown in Fig. 17. Although our survey covers a limited portionof the outer Galactic plane of about 20◦ × 2.5◦ , we found thatthe conversion factor is far from being constant, spanning about
one order of magnitude from pixel to pixel, indicating that theemitting regions are in very different physical states. This result
is in agreement with what is found in other new-generation CO
surveys (e.g. Barnes et al. 2015; Schuller et al. 2017), where thesub-arcmin resolution allows to resolve the internal structure of
the ISM up to distances of a few kiloparsecs, and sample regionswith very different values of excitation temperature and opticaldepth of the 12 CO (1–0) transition as well as possible variationsof the CO abundance. Since a constant conversion factor is not agood approximation for our data, as already mentioned, we preferred to estimate the mass of the MCs of our catalogue fromthe Herschel data. Indeed, Herschel data have a spatial resolution similar to that of our FQS data and do not suffer from thesaturation problems of the 12 CO (1–0) line that is expected to be
optically thick in large parts of the MCs.
On the other hand, a mean value of XCO can still be
useful when dealing with low-spatial-resolution data or unresolved structures, and when only CO data are available.
We therefore calculated the mean value of the XCO conversion factor for the 12 CO (1–0) line and we found XCO =(3.3 ± 1.4) × 1020 cm−2 (K km s−1 )−1 , slightly higher than but
still compatible, within the statistical error, with the valueof 2 × 1020 cm−2 (K km s−1 )−1 recommended by Bolatto et al.
(2013) for the Milky Way in their review paper. Interestingly,
the XCO derived from FQS data is very similar to that derived
in the ThrUMMS survey, a new generation CO (1–0) survey with an effective spatial resolution of 7200 , which finds
a median value of 3.8 × 1020 cm−2 (K km s−1 )−1 (Barnes et al.
2015). For 13 CO (1–0), we find a mean value of the conversion factor of (1.2 ± 0.4) × 1021 cm−2 (K km s−1 )−1 , consistent with the value (1.08 ± 0.19) × 1021 cm−2 (K km s−1 )−1 foundin the science demonstration field of the SEDIGISM survey(Schuller et al. 2017) at l = 340.0◦ –341.5◦ . It is worth noting
that, although our method to derive the constant conversion factor is different from that used in the ThrUMMS and SEDIGISM
surveys, we find consistent values. Indeed, we derive the H2 column density from Herschel data that is from the far-infraredemission of the cold dust and assuming a gas-to-dust mass
ratio, while both Barnes et al. (2015) and Schuller et al. (2017)derived the H2 column density from only CO data, measuring
the line opacity and excitation temperature from several lines
and isotopologues and assuming a CO chemical abundance withrespect H2 . Moreover, FQS observed only molecular gas outside the solar circle, that is at a galactocentric distance of Rgal &8.3 kpc, while both ThrUMMS and SEDIGISM surveys mapped
regions mainly inside the solar circle. The similarity of all theseestimates of XCO , as well the agreement with the estimate of(3.3 ± 1.7) × 1020 cm−2 (K km s−1 )−1 based only on 12 CO (1–0)
data in a sample of MCs of the far outer Galaxy (15.7 < Rgal <20.2 kpc; Brand & Wouterloot 1995) suggests that XCO is invariant, or at least not strongly variable, with galactocentric distance.

9. Conclusions
We present the FQS project, a survey in the 12 CO (1–0) and13
CO (1–0) lines of the Milky Way plane in the range 220◦ <l < 240◦ and −2◦.5 < b < 0◦ , carried out with the 12 m ARO
antenna. The median value of rms of the single tiles ranges from0.79 K to 1.31 K for 12 CO (1–0) and from 0.33 K to 0.58 K for
13
CO (1–0) at the instrumental spectral resolution of 0.65 km s−1 .

M. Benedettini et al.: The Forgotten Quadrant Survey

We built a mosaic of all the spectra in spectral cubes with spatial pixels of 1700. 3 and velocity channels of 1 km s−1 . The medianvalue of the rms in these final spectral cubes is 0.53 K and 0.22 K
for 12 CO (1–0) and 13 CO (1–0), respectively.
One of the main goals of the FQS project is to study thelarge-scale distribution of the ISM in the mapped portion of theouter Galaxy. For this purpose, we produced a catalogue of MCs
and studied their global properties. The identification of the MCs
and the measurement of their physical parameters were carriedout using the 12 CO (1–0) spectral cubes. We identified 263 MCs,
many more than previously found in the same area from theDame et al. (2001) data. The clouds are grouped in three main
structures corresponding to the Local, Perseus, and Outer armsof the Galaxy, up to a distance of ∼8.6 kpc from the Sun. This is
the first self-consistent statistical catalogue of molecular cloudsof the outer Galaxy obtained with a subarcminute spatial resolution. Such a resolution allows us to detect not only the classical giant molecular clouds but also the small clouds and toresolve the cloud structure at the subparsec scale up to a distance of a few kiloparsec. Indeed, the median value of the spherical equivalent radius of the detected structures is 1.8 pc. The
mass of the detected clouds ranges from a few tens to ∼105 M ,while the distribution of their average surface density spans arelatively narrow range around the median value of 65 M pc−2 ,indicative of a tight correlation between the cloud mass and thesize.
The correlation of both the velocity dispersion and the virial
parameters with the equivalent spherical radius suggests that
there could be a change in the properties of the identified structures at a radius of 1–2 pc. Molecular structures with sizes above
a few parsecs are typical MCs that may be self-gravitating,with non-thermal motions dominated by supersonic turbulence.
Structures smaller than ∼1–2 pc are not gravitationally boundand could dissolve unless confined by external pressure. In addition, the velocity dispersion of these clouds does not appear todepend on their size, which suggests an origin in large-scale
motions such as density wave shocks in spiral arms. The smallest structures with radii of only a few tenths of a parsec could
represent the high-density clumps of more extended clouds, themost tenuous part of which would be undetected due to lack ofsensitivity.
We used the ratio between the H2 column density derived
from Herschel data and the integrated intensity of the FQS
observed CO lines to calculate the XCO conversion factor for both12
CO (1–0) and 13 CO (1–0). We found that the conversion factor
spans about one order of magnitude from pixel to pixel as a consequence of the capacity of our data to resolve the internal structureof the MCs composed of regions in very different physical states.
The mean values of XCO are (3.3 ± 1.4) × 1020 cm−2 (K km s−1 )−1and (1.2 ± 0.4) × 1021 cm−2 (K km s−1 )−1 for 12 CO (1–0) and13
CO (1–0), respectively, which are very similar to the values
obtained in other new-generation CO surveys of comparable spatial resolution and in agreement, within the uncertainties, witholder estimates.
Acknowledgements. FQS members thank the ARO 12-m telescope operators:
Michael Begam, Kevin Bays, Robert Thompson, Clayton Kyle for their profes-

sional assistance and pleasant company during the long observational campaign.
MM acknowledges support from the grant 2017/23708-0, São Paulo Research
Foundation (FAPESP).

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A&A 633, A147 (2020)

Appendix A: Additional table
Table A.1. Properties of the molecular clouds identified in the 12 CO (1–0) data cubes.
Index
1
3
4
7

Name

l
(◦ )

FQS-MC219.856-2.190
FQS-MC219.921-2.098
FQS-MC220.030-1.363
FQS-MC220.158-1.482

219.8564
219.9211
220.0299
220.1583

b
(◦ )

ma
(00 )

−2.1897 74.24−2.0981 75.33−1.3631 74.14−1.4822 119.40

vLSR
σv
ICO
LCO
d
(km s−1 ) (km s−1 ) (K km s−1 ) (K km s−1 pc2 ) (kpc)

mi
(00 )

PA
(◦ )

22.85
42.80
45.43
31.85

84.01
52.40
113.61
55.38

28.49
31.76
17.23
70.09

0.80
1.00
0.70
1.01

10.7128.437
5.851
22.190R(pc)

A(pc2 )M(M )Σ
(M pc−2 )

αvir

nl

flag1

flag2

0.889
1.393
0.709
4.509

2.30
4.91
0.98
52.83

0.00
18.37
44.49
2888.87

0.00
3.74
45.27
54.68

0.00
88.34
9.06
1.86

1
1
1
1

1
1
0
0

2
0
2
0

13.97
26.20
3.71
229.31

2.332
2.649
1.319
7.896

Notes. Columns are as follow: Col. 1: progressive index. Column 2: name defined as “FQS-MC” followed by the Galactic coordinates of thecloud’s centroid. Columns 3 and 4: Galactic longitude and latitude of the cloud’s centroid. Columns 5 and 6: intensity-weighted semi-major andsemi-minor axes, ma and mi, respectively. Column 7: position angle with respect to the cube x-axis. Column 8: mean velocity. Column 9: velocity
dispersion. Column 10: 12 CO (1–0) integrated intensity across the area of the cloud. Column 11: total luminosity from the 12 CO (1–0) line. Column
12: kinematic distance from the Sun. Column 13: equivalent radius, assuming circular geometry. Column 14: area. Column 15: total mass derived
from Herschel H2 column density. Column 16: average surface density. Column 17: virial parameter. Column 18: number of dendrogram leaves.
Column 19: flag1; 0 indicates that the cloud is fully mapped in the Herschel H2 column density map, 1 indicates that the area of the cloud asderived from the 12 CO (1–0) data is only partially covered, or not covered at all, by the Herschel H2 column density map; for those clouds thederived mass and surface density are lower limits and the virial parameter is an upper limit. Column 20: flag2; 0 means that the cloud is fully
mapped in 12 CO (1–0), 2 means that the area of the cloud as derived from the 12 CO (1–0) touches the border of the map; those clouds could extend
outside the mapped area, therefore the measured parameters are linked to an uncertainty that depends on how much CO emission was missed.
Only a few objects are listed as example here, the complete catalogue is available at CDS.

A147, page 16 of 16