A Galactic Molecular Cloud Clump Catalog from Hi-GAL Data: Method and Initial Results Comparison to BGPS
OOctober 2, 2018
Preprint typeset using L A TEX style AASTeX6 v. 1.0
A GALACTIC MOLECULAR CLOUD CLUMP CATALOG FROM HI-GAL DATA: METHOD AND INITIALRESULTS COMPARISON TO BGPS
Erika Zetterlund, Jason Glenn
CASA, Department of Astrophysical and Planetary Sciences, University of Colorado 389-UCB, Boulder, CO 80309, USA
Erik Rosolowsky
Deptartment of Physics, University of Alberta, Edmonton, Alberta, Canada
ABSTRACTAs the precursors to stellar clusters, it is imperative that we understand the distribution and physicalproperties of dense molecular gas clouds and clumps. Such a study has been done with the ground-based Bolocam Galactic Plane Survey (BGPS). Now the
Herschel infrared GALactic plane survey(Hi-GAL) allows us to do the same with higher quality data and complete coverage of the Galacticplane. We have made a pilot study comparing dense molecular gas clumps identified in the Hi-GALand BGPS surveys, using six 2 ◦ × ◦ regions centered at Galactic longitudes of (cid:96) = 11 ◦ , 30 ◦ , 41 ◦ , 50 ◦ ,202 ◦ , and 217 ◦ . We adopted the BGPS methodology for identifying clumps and estimating distances,leading to 6198 clumps being identified in our substudy, with 995 of those having well-constraineddistances. These objects were evenly distributed with Galactic longitude, a consequence of Hi-GALbeing source confusion limited. These clumps range in mass from 10 − M (cid:12) to 10 M (cid:12) , and haveheliocentric distances of up to 16 kpc. When clumps found in both surveys are compared, we seethat distances agree within 1 kpc and ratios of masses are of order unity. This serves as an externalvalidation for BGPS and instills confidence as we move forward to cataloging the clumps from theentirety of Hi-GAL. In addition to the sources that were in common with BGPS, Hi-GAL found manyadditional sources, primarily due to the lack of atmospheric noise. We expect Hi-GAL to yield 2 × clumps, with 20% having well-constrained distances, an order of magnitude above what was found inBGPS. INTRODUCTIONSignificant observational and theoretical progress has been made in the study of star formation. However, crucialaspects, such as why the stellar and star cluster initial mass functions appear uniform across many Galactic environ-ments, remain unexplained. In order to understand stellar clusters and OB associations, it is critical to understandthat from which they are formed. Thus the study of molecular cloud clumps has become a primary focus in the fieldof high-mass star formation (e.g., McKee & Ostriker 2007). Large-scale studies of the Galactic dense molecular gas —its distribution and properties — are necessary in order to challenge models of galaxy evolution and reveal the originof the stellar cluster initial mass function.Such large-scale studies have recently become practical with the actualization of Galactic Plane dust continuumsurveys at sub/millimeter wavelengths [BGPS: Aguirre et al. (2011); Ginsburg et al. (2013); ATLASGAL: Schulleret al. (2009); Csengeri et al. (2014, 2016); Hi-GAL: Molinari et al. (2010, 2016)]. These surveys have detected tens ofthousands of molecular cloud clumps and cores, which can now be extracted and used to study physical properties ofhigh-mass star formation regions. The census of Galactic dense molecular cloud structures enabled by these surveyswill constrain star formation and galaxy evolution theories (e.g., Kennicutt & Evans 2012). Such studies of physicalproperties (e.g., Peretto & Fuller 2010; Giannetti et al. 2013; Elia et al. 2013) and mass distributions (e.g., Netterfieldet al. 2009; Olmi et al. 2013, 2014; G´omez et al. 2014; Ellsworth-Bowers et al. 2015; Wienen et al. 2015) have begun,but have yet to reveal a coherent story concerning the evolution of the dense interstellar medium and the uniformity [email protected] a r X i v : . [ a s t r o - ph . GA ] M a y Zetterlund et al. of the stellar cluster mass function.Fortunately, theoretical models of the dense interstellar medium are beginning to produce predictions robust enoughto be used in conjunction with observational data to constrain the molecular cloud clump mass function (e.g., Donkovet al. 2012; Veltchev et al. 2013). This function is generally expected to take either a power-law or lognormal form,with each distribution corresponding to a different physical process in the molecular clouds. Gravitational collapse ofdense structures would produce a power-law (e.g., Padoan & Nordlund 2002). A lognormal density distribution can beproduced by supersonic turbulence (e.g., Padoan et al. 1997), although such conditions are not necessary (Tassis et al.2010). Likely, these processes are interacting within the dense molecular clouds, producing a mass function displayinga combination of these forms (e.g., Offner et al. 2014; Hopkins 2013). Which mode dominates will have implicationsfor the competing theories of high-mass star formation (Elmegreen 1985).On a grander scale, the distribution of dense molecular gas in the Milky Way has implications for using our Galaxy asthe ground truth for studies of galaxy evolution. Until recently, numerical models had substantial difficulty generatinglong-lived spiral galaxies. This was due either to rapid depletion of gas before the disks could be sustained or thegalaxies blowing themselves apart with an inadequate balance of gravity and stellar feedback. In the past two decades,remarkable progress has been made towards reproducing observed properties of galaxies (e.g., Katz et al. 1996; Kereˇset al. 2009; Stinson et al. 2013). Currently, simulations that incorporate feedback into the interstellar medium fromstar formation and the late stages of massive stellar evolution can reliably create large spiral galaxies. However,the star formation, being unresolved in the simulations, uses prescriptive recipes tuned a posteriori. Typically, starformation is turned on at a fixed interstellar medium density threshold (e.g. 10 cm − ), at which point ∼
1% of thegas mass is converted into stars (Kim et al. 2014). Unfortunately, numerical simulations have largely been unable toa priori produce the strong winds and inefficiency of star formation observed in galaxies (Hopkins et al. 2014). Thedense molecular gas distribution is an observational key to probing the star formation efficiency on scales resolvableby simulations. Because the amount and rate of star formation drives feedback, it is imperative that these recipes aretested against reality.We will use the
Herschel Space Observatory’s
Hi-GAL survey, which provides complete coverage of the GalacticPlane in the far-infrared to submillimeter wavebands, to create such a map and test these explanations of clumpmass functions and galaxy evolution models. This paper describes the method we will be using to identify Hi-GALmolecular cloud clumps and determine their heliocentric distances. It will furthermore compare the results of anumber of representative test regions to BGPS, a well-vetted benchmark. However, we expect Hi-GAL to significantlysupersede BGPS. Molinari et al. (2016) have independently begun cataloging the dense molecular cloud clumps foundin the Hi-GAL survey. They have identified and extracted photometry for clumps found in the majority of the innerGalaxy in all five of
Herschel’s photometric bands. This work serves as a complement to the work of Molinari et al.(2016) through the use of a fundamentally different source identification technique. In addition, we go beyond theirwork in the determination of distances and physical properties of the clumps.Dense molecular gas structures can be divided into three categories: clouds, clumps, and cores. Molecular cloudshave denser substructures called molecular cloud clumps, which in turn have even denser substructures called molecularcloud cores. These cores are gravitationally bound and will form individual stars or simple stellar systems. Typicalradii are clouds R = 1 − . R = 0 . − . R = 0 . − . −
500 cm − , clumps 10 − cm − , and cores 10 − cm − (Bergin & Tafalla 2007). Cores are onlyresolvable within a couple kiloparsecs by single-dish telescopes. Clumps are detectable within ∼ Herschel . Farther out we can only resolve entire clouds (Dunham et al. 2011). DATAPreviously, the Bolocam Galactic Plane Survey (BGPS) was made with the Caltech Submillimeter Observatory andwas released in two versions (Aguirre et al. 2011; Ginsburg et al. 2013). BGPS was a λ = 1 . − ◦ < (cid:96) < ◦ and latitudes | b | < . ◦ , plus an additional 20 deg . From the survey, a catalog of 8,594(Version 2) sources was produced, 20% of which have well constrained distances. Even with its incomplete coverage ofthe Galactic Plane, BGPS showed the emergence of spiral arms, as well as evidence for significant amounts of denseinterarm gas (Ellsworth-Bowers et al. 2015).The Herschel infrared GALactic plane survey (Hi-GAL) (Molinari et al. 2010) originally planned to observe a 2 ◦ strip covering | (cid:96) | < ◦ , but was extended to include the entire 360 ◦ of the Galactic plane. Using the SPIRE (Griffinet al. 2010) and PACS (Poglitsch et al. 2010) instruments aboard the Herschel Space Observatory (HSO), this surveyobserved in wavebands at 70, 160, 250, 350, and 500 µ m. SPIRE (250, 350, and 500 µ m) observed thermal dustemission, which is concentrated in dense molecular gas clumps, whereas PACS (70 and 160 µ m) observed dense gas MC Clump Catalog: Method and Initial Results (cid:12) (5 σ ) at distances up to 20 kpc(corresponding with a 1 σ RMS of 100 mJy at 250 µ m, assuming a temperature of 20 K).In this paper, we analyzed a representative sample of six Hi-GAL regions, centered at (cid:96) = 11 ◦ , 30 ◦ , 41 ◦ , 50 ◦ , 202 ◦ ,and 217 ◦ . This sample includes regions from both inner and outer portions of the Galaxy, and, with the exceptionof (cid:96) = 202 ◦ , have a significant overlap with BGPS observations. Figure 1 shows the maps for the (cid:96) = 30 ◦ and 217 ◦ regions. Emission levels vary strongly depending on proximity to the Galactic center, but there is extensive structurein all regions. GLON −1.0−0.50.00.51.01.5 G L A T J y / b e a m GLON −2.0−1.5−1.0−0.50.00.5 G L A T J y / b e a m Figure 1 . Example maps in SPIRE 500 µ m from our representative sample of Hi-GAL — (cid:96) = 30 ◦ ( left ), which looks throughthe edge of the Galactic bar, and (cid:96) = 217 ◦ ( right ). Emission levels are significantly higher in the inner Galaxy than the outerGalaxy due both to the greater quantities of molecular gas, and to the longer optical path integrating more emission.3. METHOD3.1.
Map Making
The Hi-GAL observations are stored in the Herschel Science Archive (HSA) and the Herschel Interactive ProcessingEnvironment (HIPE) allows users to access the data. The tool UniHIPE was used to format the HSA data suitableto be input into the Hi-GAL mapping pipeline, Unimap (Piazzo et al. 2012, 2015a,b).Each Hi-GAL observation is composed of two orthogonal scans. Unimap takes the time-ordered bolometer datafrom each scan and combines them to make a single astrophysical map, along with various evaluation maps. Thepipeline begins by cleaning the time-ordered data. It removes the offset in the input data, then finds and removessignal jumps and glitches, both of which can be caused by cosmic rays. Finally, the drift, a low frequency signal dueto slowly varying bolometer temperatures, is fit and removed. The time-ordered data are then made into a map, usingan iterated Generalized Least Squares method. This method often leads to cross-like artifacts on bright sources, whichare removed by Unimap’s post-processing. 3.2.
Filtering
The 500 µ m SPIRE band has the lowest resolution of the bands, with a beam FWHM of 32 (cid:48)(cid:48) .
2, and so we convolvedand rebinned all maps to 500 µ m resolution to enable multiband photometry. Foreground cirrus clouds are presentin all of the maps. Since this undesirable flux density is present on angular scales larger than that of molecularcloud clumps, the maps were high-pass filtered on the 3 (cid:48) scale to remove this flux density. The filtering was done infrequency-space using an inverted 2D Gaussian window with σ corresponding to 3 (cid:48) . This scale was chosen for tworeasons. Firs t, it is a larger angular scale than the largest of the BGPS clumps. Second, when the Hi-GAL maps Zetterlund et al. were filtered on a larger scale of σ = 5 (cid:48) , nearly all of the clumps found were smaller than 3 (cid:48) . Thus, this filtering scaleremoves as much extended emission as possible with minimal attenuation of molecular cloud clump sizes and fluxdensities (and therefore inferred masses).Simulations were performed in order to quantify the effects of the high-pass filter. Synthetic Gaussian objects wereinserted onto both the (cid:96) = 30 ◦ map, and onto constant background maps. Synthetic sources were distributed randomly,but were restricted to have their centers no closer than three times the source FWHM. The maps were then filteredand objects identified by Bolocat (See Section 3.3) on the constant background map. These source masks were thenused to calculate source properties on synthetic sources from the (cid:96) = 30 ◦ background map. This was done for filteringscales of σ = 2 . (cid:48) , 3 . (cid:48) , 4 . (cid:48) , and 4 . (cid:48) , and with various synthetic source sizes. For each combination of filtering scaleand source size ∼ S ), peak flux density ( S pk ), and FWHM. Our mean clump size corresponds most closely to thesecond smallest synthetic object size (1 . (cid:48) ), and 90% of our objects are smaller than the dashed line. Thus Hi-GALclumps will have their integrated flux density attenuated by (20 ± σ = 3 (cid:48) .Similarly, peak flux density will be attenuated by < ± Simulated object FWHM [arcminutes] I n t e g r a t e d f l u x d e n s i t y a tt e nu a t i o n f a c t o r Simulated object FWHM [arcminutes] P e a k f l u x d e n s i t y a tt e nu a t i o n f a c t o r Simulated object FWHM [arcminutes] F W H M a tt e nu a t i o n f a c t o r σ =4.8 ′ σ =4.1 ′ σ =3.2 ′ σ =2.4 ′ Figure 2 . Attenuation factors for integrated flux density ( left ), peak flux density ( center ), and FWHM ( right ). Maps ofsimulated objects are filtered at various spatial scales. Plotted are the median attenuation factors of objects identified byBolocat. The dashed line indicates the 90 th percentile in angular size for objects in our Hi-GAL substudy. While the majority of the observation regions are covered by both of
Herschel’s orthogonal scans, the edges arenot. We mask off the regions which are not covered by both scans to exclude these lower-quality data and to avoidconfusing the clump-finding code with the original ragged map edges. This removed 29% of flux-containing pixels. Wealso masked off regions where the high-pass filter added flux density, as opposed to removing it. This was a consequenceof the flux density on large scales being negative in these areas, and thus subtracting those negative values added fluxdensity to the pixels. These areas were located at high galactic latitude, where flux density was low. This removed anadditional 3 − Source Identification
Source identification was done on 500 µ m SPIRE maps using Bolocat, a seeded watershed algorithm tool developedfor BGPS (Rosolowsky et al. 2010). Clumps are found by first identifying regions of significant emission, wheresignificance is determined in units of the local noise estimate. These regions of high significance are then expandedinto adjacent lower significance areas, eliminating artificial small-scale structure. Finally, regions are split into multipleclumps, where appropriate, based on local maxima.Hi-GAL is confusion limited by cirrus, large-scale structure, and source confusion that in BGPS were attenuated byan atmospheric subtraction algorithm. The flux density level of clumps which we can identify in each map is limitedby the emission levels in the map. This is demonstrated in the power spectral densities (PSDs) in Figure 4. Thereis greater power along lines of sight with more interstellar medium (ISM), as well as greater power on larger scales,which may be due to source confusion or the sizes of GMCs themselves. A typical GMC with a radius of 10 pc (e.g.Solomon et al. 1987) would have to be >
23 kpc away in order to have a 3 (cid:48) extent.
MC Clump Catalog: Method and Initial Results GLON −1.5−1.0−0.50.00.51.0 G L A T GLON −1.5−1.0−0.50.00.51.0 G L A T GLON −1.5−1.0−0.50.00.51.0 G L A T Figure 3 . Masking the (cid:96) = 11 ◦ region. Left : The complete high-pass filtered map. The two scan directions can be seen.
Center :Pixels not covered by both scans have been masked off.
Right : Pixels to which the high-pass filter added flux density have beenmasked off. inverse arcminutes l o g ( J y b e a m − a r c m i nu t e − ) ℓ =30 ◦ ℓ =41 ◦ ℓ =202 ◦ Figure 4 . The PSDs for a representative sample of regions. Pre- and post-filtering are shown as dashed and solid lines,respectively. Emission levels on all spatial scales decrease with distance from the Galactic center and lines of sight through theinner Galaxy have greater integrated flux density than towards the outer Galaxy.
With more overall emission in regions nearer the Galactic center, sources in those regions must be brighter for usto detect them above the confusion noise. Therefore we used a constant-noise map for each region, with each “noise”map’s value determined by the emission level in its respective emission map. This was done by fitting the distributionflux densities of pixels with positive emission to an exponential function. The noise value was taken to be proportionalto the scale factor of the exponential fit, specifically 0 . λ , where λ is the scale factor. As an objective choice was notpossible, this selection was made as it consistently produced object contours which matched our visual expectationsacross regions at widely varying Galactic longitudes. The specific value of 0 . λ for the noise was chosen after anexploration of Bolocat’s parameter space, which was done using the (cid:96) = 41 ◦ region as a test map.The threshold for detection and the level down to which areas meeting the detection threshold were expanded weredetermined first. We chose to set the threshold at 3 σ and to expand down to 1 σ , where σ is the “noise” level. Thesevalues were chosen as they successfully included the flux density which had the appearance of real structure. A divisioncriterion of 2 σ was then decided upon. That is, if the saddle between two local maxima is different from the maximaby at least 2 σ , then the clump is divided. This was chosen as it produced results consistent with what was seen in theflux density contours of the test map, a sample section of which can be seen in Figure 5. The initial threshold anddivision criterion were the same in BGPS, which only expanded down to 2 σ .Choosing a higher threshold would have decreased the number of sources found, as would have choosing a higher Zetterlund et al.
GLON −0.85−0.80−0.75−0.70−0.65−0.60−0.55 G L A T −1.0−0.8−0.6−0.4−0.20.00.20.40.60.81.0 J y / b e a m Figure 5 . Subsection of the test map for determining Bolocat parameters. Thick black contours show object borders. Thinblue contours show flux density levels with a step size of 2 σ , starting at zero. floor down to which to expand the regions, since clumps must be at least a beam in size. A higher expansion floorwould have also decreased the size of clumps, particularly further from the mid-plane, where flux density is lower.Choosing a lower threshold for splitting clumps would also have led to smaller clumps, although clumps sizes would belimited by the requirement that local maxima must be separated by at least two beam widths. Our choices for theseparameters were chosen to match our visual expectations in the test map, attempting to err on the side of a higherdetection threshold and less clump division.The high-pass filter also has associated systematic effects. A more aggressive filter would have resulted in smallerclumps. More significantly, the high-pass filter affects the flux densities of the identified clumps. Figure 6 compareshigh-pass filtered and unfiltered flux densities in the (cid:96) = 50 ◦ region. Clumps were identified in the filtered map, withphotometry re-calculated using the unfiltered maps for the comparison. Photometry was done by summing the fluxdensity found in the pixels within the source boundary, as opposed to background-subtracted aperture photometry.No clump exceeds a flux density ratio of unity by construction, as the small percentage of pixels where the filter addedflux density were excluded from source finding.There is a trend towards the clump containing a higher fraction of the total flux density along the line of sight forhigher flux density clumps. On the other hand, the faintest clumps have a wide range of flux density ratios. Thesetrends are seen in all of the regions in this substudy.3.4. Distance Determination
This distance determination method was developed for BGPS by Ellsworth-Bowers et al. (2013) and Ellsworth-Bowers et al. (2015), and resulted in 1,710 well-constrained distances. Full details of the method are available inEllsworth-Bowers et al. (2013, 2015), and a summary is provided here. To construct a 3D map from the sourcesidentified in Hi-GAL and to compute their physical properties, heliocentric distances must be determined. Everyline of sight has a function V r ( d (cid:12) ), found from a Galactic rotation curve, describing radial velocity as a function ofheliocentric distance. Kinematic distances can be determined by matching a clump’s v LSR to V r ( d (cid:12) ). However, withinthe inner Galaxy, this information results in two possible kinematic distances and an ambiguity as to which is thetrue distance. This ambiguity will be broken using probabilistic methods in a Bayesian framework. A unique distance MC Clump Catalog: Method and Initial Results S hpf [Jy] −1.6−1.4−1.2−1.0−0.8−0.6−0.4−0.20.0 l og ( S hp f / S un f ) −1 0 1 2 3 4 log (S hpf ) −1.6−1.4−1.2−1.0−0.8−0.6−0.4−0.20.00.2 l og ( S hp f / S un f ) ℓ =50 ◦ | b | Figure 6 . Comparison of high-pass filtered and unfiltered flux densities for the (cid:96) = 50 ◦ region. S hpf is the clump’s total fluxdensity, taken from the high-pass filtered map in which it was identified, and S unf is the total flux density in the unfiltered mapwhich lies within the clump’s border. Colors correspond to absolute value of Galactic latitude. probability density function (DPDF) will be calculated for each source. The posterior DPDF is found byDPDF = L ( d (cid:12) | (cid:96), b, v LSR ) (cid:89) i P i ( d (cid:12) | (cid:96), b ) , (1)where L ( d (cid:12) | (cid:96), b, v LSR ) is the kinematic distance likelihood, and the P i ( d (cid:12) | (cid:96), b ) are various Bayesian priors which willhelp determine which distance has the higher probability of being true. Once DPDFs are calculated, they can be drawnfrom in Monte Carlo simulations, even when distances are not well constrained, and thus cloud clump properties canbe characterized, with robust uncertainties.The rotation curve from the Reid et al. (2014) BeSSeL survey, derived from maser parallax measurements to sitesof high-mass star forming regions ( − ◦ ≤ (cid:96) ≤ ◦ ), was paired with line-of-sight velocities derived from the COGalactic Ring Survey (GRS) to determine the kinematic distances to the molecular cloud clumps. Because there areoften multiple velocity components in GRS spectra, each source spectrum is created by subtracting the spectrum ofan off-source region from that of the on-source region. The off-source region is determined by creating a rind aroundthe source mask, then excluding pixels associated with other Hi-GAL sources. In this way we eliminate the velocitycomponents not associated with the clump from the clump’s spectrum (see Ellsworth-Bowers et al. 2015, Section 4).Dense gas tracer observations of specific sources, originally made for BGPS, were also utilized. These observationswere associated with the new Hi-GAL sources through the object masks obtained from Bolocat. The most powerful P i implemented in BGPS used absorption by the clumps of diffuse Galactic mid-infrared emission near λ = 8 µ m.When the majority of this diffuse emission lies in the background, the molecular cloud clump is called an infrared darkcloud (IRDCs) (Perault et al. 1996; Simon et al. 2006; Peretto & Fuller 2009; Battersby et al. 2011). Ellsworth-Bowerset al. (2013) defined the term 8 µ m absorption feature to include molecular cloud clumps which exhibit any λ = 8 µ m intensity decrement, thus allowing for absorption less pronounced than seen in IRDCs. The P i developed to takeadvantage of this feature uses the Galactic infrared emission model of Robitaille et al. (2012) to simulate clumps atvarious heliocentric distances. These simulated images are then compared to the corresponding GLIMPSE (Churchwellet al. 2009) IRAC Band 4 image, using a χ statistic, to generate the P i .The EMAF morphological matching of synthetic GLIMPSE based on Hi-GAL flux density and actual GLIMPSEmaps works far better with high-pass filtered maps than with unfiltered maps. In the unfiltered maps, the presenceof cirrus emission contributes too much flux to the clumps, causing them to be placed much further away than isplausible. Use of a σ = 3 (cid:48) Gaussian high-pass filter removes the cirrus emission and remedies this problem. Twoexamples from the (cid:96) = 41 ◦ region are shown in Figure 7.Three additional P i s were developed by Ellsworth-Bowers et al. (2013). First, the molecular hydrogen (H ) uses Zetterlund et al.
MAP G a l a c t i c La t i t ude [ deg ] (a) Synthetic G a l a c t i c La t i t ude [ deg ] (b) MAP G a l a c t i c La t i t ude [ deg ] (c) GLIMPSE R e l a t i v e P r obab ili t y d tan (d) G L I M PSE I n t en s i t y [ M Jy s r - ] M AP F l u x D en s i t y [ Jy bea m - ] MAP G a l a c t i c La t i t ude [ deg ] (a) Synthetic G a l a c t i c La t i t ude [ deg ] (b) MAP G a l a c t i c La t i t ude [ deg ] (c) GLIMPSE R e l a t i v e P r obab ili t y d tan (d) G L I M PSE I n t en s i t y [ M Jy s r - ] M AP F l u x D en s i t y [ Jy bea m - ] Figure 7 . Two examples of EMAF morphological matching in the (cid:96) = 41 ◦ region. Panels (a) on the left and right show thesynthetic GLIMPSE map based on the SPIRE 500 µ m map shown in the (b) panels. The synthetic maps shown are the bestmatch to the GLIMPSE map shown in panels (c). Pixels with 8 µ m emission instead of absorption were excluded from themorphological matching. DPDFs are shown in panels (d), with black and red corresponding to EMAF and posterior DPDFs,respectively. the small scale height of molecular gas in the disk to constrain clumps at high latitudes to be at their near kinematicdistances. This is done using the three-dimensional model of the distribution of molecular gas in the disk of Wolfireet al. (2003). Second, the maser parallax P i uses distances measured by the BeSSeL survey to precisely determinedistances to its 100+ cataloged sources. Third, the known distances P i associates molecular cloud clumps with giantmolecular clouds (GMCs) found in the GRS catalog of Rathborne et al. (2009), and uses the GMC physical propertiesderived by Roman-Duval et al. (2010). RESULTS: COMPARISON TO BGPSA visual comparison of Hi-GAL and BGPS using the (cid:96) = 11 ◦ region is seen in Figure 8. The Hi-GAL map is shownunfiltered and high-pass filtered, but the clumps were identified in the filtered map. As is readily seen, there are manymore clumps found in Hi-GAL than over the same region of BGPS. Furthermore, the clumps are larger in Hi-GALthan they were found to be in BGPS. Comparing the Hi-GAL clump borders with the BGPS maps shows that muchof what looked like 1/f noise in BGPS was actually sources which failed to meet the detection criteria. Hi-GAL’shigher sensitivity to slightly larger angular scales allows us to identify this flux as coming from clumps with greaterconfidence than was possible with BGPS. Conversely, BGPS identified almost nothing which was not identified inHi-GAL, confirming the low false positive rate of BGPS, which was previously derived from simulations.4.1. Clump Densities on the Sky
Table 1 shows the number of clumps in a selection of complete Hi-GAL regions, as well as all of BGPS. Eachregion is 2 ◦ × ◦ . Listed are the number of total clumps, the number of clumps with some distance information, andthe number of clumps with well-constrained distances. Clumps were considered to have “well-constrained” distancesif their DPDFs had a FW ≤ . ≤ . (cid:96) = 11 ◦ , ◦ , or 217 ◦ , but there are directed observations of dense gas tracers in (cid:96) = 11 ◦ . There is a slight tendencytowards more clumps being identified in regions with lower levels of confusion, such as those in the outer galaxy. MC Clump Catalog: Method and Initial Results GLON −0.6−0.4−0.20.00.20.40.6 G L A T J y / b e a m GLON −0.6−0.4−0.20.00.20.40.6 G L A T −3.0−1.50.01.53.04.56.07.59.0 J y / b e a m GLON −0.6−0.4−0.20.00.20.40.6 G L A T J y / b e a m Figure 8 . Comparison of molecular cloud clumps identified in the same (cid:96) = 11 ◦ region of Hi-GAL 500 µ m ( upper : unfiltered left , high-pass filtered right ) and BGPS ( lower ). Hi-GAL’s higher S/N and absence of atmospheric emission allow for moreclumps to be identified than in the ground-based BGPS. Table 1 . Distance Statistics
Region a Total Some distance Well-constrainedclumps information distance (cid:96) = 11 ◦
875 96 40 (cid:96) = 30 ◦
979 588 320 (cid:96) = 41 ◦ (cid:96) = 50 ◦
923 582 392 (cid:96) = 202 ◦ (cid:96) = 217 ◦ b Table 1 continued on next page Zetterlund et al.
Table 1 (continued)
Region a Total Some distance Well-constrainedclumps information distance a Each region is Hi-GAL unless otherwise specified, with Hi-GAL regions having dimensions of 2 ◦ × ◦ . b Data are for the entirety of BGPS.
Properties of clumps individually could only be compared where clumps identified in each survey aligned with oneanother. Therefore the overlapping subsections of each map pair were studied for such clumps. Table 2 compares thenumber of clumps found in these overlapping Hi-GAL and BGPS subregions. Listed are the number of total clumps,the number of clumps with some distance information, and the number of clumps with well-constrained distances. Thereduction in number of Hi-GAL clumps in Table 2 as compared to Table 1 is primarily due to the smaller Galacticlatitude range covered by BGPS as compared to Hi-GAL. Note that the regions are not consistently sized, and thuscomparing numbers between regions is not a useful exercise. Information on the individual clumps being compared islisted in the Appendix.
Table 2 . Distance Statistics: Overlapping Subregions
Region Area Survey Total Some distance Well-constrained(deg ) clumps information distance (cid:96) = 11 ◦ (cid:96) = 30 ◦ (cid:96) = 41 ◦ (cid:96) = 50 ◦ (cid:96) = 202 ◦ (cid:96) = 217 ◦ In the (cid:96) = 11 ◦ region, the numbers would indicate more clumps with distance information in BGPS than Hi-GAL.However, upon examination of the maps, it is apparent that this is only due to 5 single Hi-GAL clumps being splitinto two BGPS clumps each, and one single BGPS clump being split into two Hi-GAL clumps. Thus the numbers ofclumps with distance information are equal in this region. There are no additional clumps with distance informationin Hi-GAL for this region due to it not being covered by GRS. The only velocities are thus from directed observationsof dense gas tracers done for BGPS. All additional distances are due to GRS. While the known distances prior, whichassociates nearby clumps with one another, was used, it did not provide any additional distances in this subset ofHi-GAL observations. The (cid:96) = 202 ◦ and 217 ◦ regions has no distance information due to the lack of GRS observationsin the outer Galaxy and a lack of directed observations.4.2. Angular Sizes
MC Clump Catalog: Method and Initial Results θ R of a clump as the geometric mean of the deconvolved major and minoraxes of the flux density distribution, θ R = η [( σ − σ )( σ − σ )] / . (2)For Hi-GAL σ beam = θ FWHM / √ (cid:48)(cid:48) , θ FWHM = 35 (cid:48)(cid:48) , and η = 2 . σ min < σ beam the deconvolved angular radius is non-real. This is the case for 14% of our sources, arising from finiteS/N. Mean angular radii for Hi-GAL and BGPS sources are 71 ± (cid:48)(cid:48) and 51 ± (cid:48)(cid:48) , respectively. Both follow log-normal distributions (with shape parameters of σ = 0 . ± .
02 and σ = 0 . ± .
01, respectively), shown in red. Weexpect that the lognormal distribution is a result of random processes in the observations (such as variations in clumpdistances and intrinsic sizes), an interpretation which is consistent with the central limit theorem. Thus, Figure 9 isintended to demonstrate the difference in typical sizes found in the two surveys. θ R [arcsec] N u m b e r Hi-GAL θ R [arcsec] N u m b e r BGPS
Figure 9 . Distributions of deconvolved angular radii with log-normal fits in red. Hi-GAL is shown on the left, including clumpsfrom all of the regions in this substudy, and has a shape parameter of σ = 0 .
34. BGPS is shown on the right (with all resolvedsources included) and has a shape parameter of σ = 0 .
37. In grey is shown the subsample of BGPS clumps found in regionsoverlapping our Hi-GAL substudy.
A comparison of the angular radii of clumps matched between Hi-GAL and BGPS is seen in Figure 10. While wedo not expect a consistent one-to-one ratio in angular radii, we do expect Hi-GAL clumps to be larger, and this isgenerally what we see, although less so with those clumps found in the (cid:96) = 30 ◦ region. Dashed lines show a factorof 2 in slope on either side of the identity line and demonstrate the dearth of objects in the lower right of the plot,as compared to the upper left. Clumps can be significantly larger in Hi-GAL due to the dimmer edges of the clumpsbeing above the noise in Hi-GAL, where they were not in BGPS. The (cid:96) = 30 ◦ region contains the edge of the Galacticbar, the bright emission and high confusion levels of which led to the high-pass filter creating more extensive negativebowls. This subtracted more flux than was ideal around the bright Galactic bar, and thus removed flux from thecomparatively dim edges of the clumps in this region, to the extent that they were found to be smaller in Hi-GALthan in BGPS. Also of note are the clumps on the left edge of this plot. These are clumps which were unresolved inBGPS, but which we can now resolve with Hi-GAL because Hi-GAL detected faint, extended emission.Figure 10 also shows the ratio of radii in the two surveys, for the same objects, but with unresolved objects excluded.The median and mean radius ratios are 1.37 and 1.61, respectively. This agrees with the difference in overall meanradii from the two surveys, as derived from Figure 9.4.3. Clump Masses
Masses can be calculated for those clumps with well constrained distances using the high-pass filtered flux densitiesat 500 µ m and the most probable distances from the clumps’ DPDFs. The conversion from flux density to mass is M = Rd (cid:12) κ B ( T ) S , (3)2 Zetterlund et al.
BGPS θ R [arcseconds] H i G A L θ R [ a r c s e c o n d s ] ℓ =11 ◦ ℓ =30 ◦ ℓ =41 ◦ ℓ =50 ◦ ℓ =202 ◦ ℓ =217 ◦ −0.8 −0.6 −0.4 −0.2 0.0 0.2 0.4 0.6 0.8 1.0 log (R HiGAL /R BGPS ) Figure 10 . Left : Angular radius in Hi-GAL plotted against angular radius in BGPS, for clumps found in both surveys. Onlythose clumps which had well constrained distances in both surveys were used for this comparison. Colors correspond to theHi-GAL region in which the clump was found. Note that in the line of unresolved BGPS clumps on the left of the plot, sourcesfrom the (cid:96) = 11 ◦ and 30 ◦ regions are covered by later regions, and not lacking. The solid line is where the radii are equal, whilethe dashed lines show a factor of two in either direction. Right : Hi-GAL to BGPS radius ratios of matching clumps, excludingthose which are unresolved. The mean log ( R HiGAL /R BGPS ) is 0.14, with a standard deviation of 0.24. where R = 100 is the gas-to-dust mass ratio, κ = 5 .
04 cm g − is the opacity at 500 µ m (Ossenkopf & Henning 1994), B ( T ) is the Planck function at 500 µ m, d (cid:12) is the heliocentric distance, and S is the integrated flux density.Battersby et al. (2011) used pixel-by-pixel modified blackbody fits of Hi-GAL data to determine that mid-infrared-dark molecular cloud clumps generally span the temperature range 15 K (cid:46) T (cid:46)
25 K. Furthermore, Dunham et al.(2011) found an NH gas kinetic temperature of (cid:104) T K (cid:105) = 17 . ± . R = θ R d (cid:12) , where θ R is angular radius and d (cid:12) is heliocentricdistance. Points are colored by heliocentric distance, d (cid:12) . If mass scaled as radius cubed, as would happen if the clumpswere of constant density, we would see a slope of 3. The figure shows a slope closer to 2 than to 3, thus indicatingcentrally concentrated masses. (The data are consistent with radial density profiles proportional to 1 /R ; however, wecaution against concluding a power-law profile in the absence of detailed modeling, including temperature profiles.)Malmquist bias is seen in that the smallest clumps are found at the nearest heliocentric distances, with the largest,most massive objects, being found distant to us. DISCUSSION
MC Clump Catalog: Method and Initial Results −1.0 −0.5 0.0 0.5 1.0 1.5 log (M HiGAL /M BGPS ) Figure 11 . Hi-GAL to BGPS mass ratios of all matching clumps. The mean log ( M HiGAL /M BGPS ) is 0.20, with a standarddeviation of 0.29. -3 -2 -1 Radius [pc] -4 -3 -2 -1 M a ss [ M ⊙ ] d ⊙ [ k p c ] Figure 12 . Mass plotted against physical radius for all resolved Hi-GAL sources with well-constrained distances. Colorscorrespond to heliocentric distance, d (cid:12) . Compared to BGPS, Hi-GAL detects more sources in all Galactic regions, due to the lack of atmospheric noise.It is especially true in the outer Galaxy, where most sources which are detected with Hi-GAL were lost below theatmospheric noise in BGPS. In BGPS the number of clumps dramatically decreased as observations moved furtherfrom the Galactic center. Hi-GAL continues to see sources far from the Galactic center, as well as at Galactic latitudesfarther from the the mid-plane.4
Zetterlund et al.
We identified a relatively constant number of clumps in each region, and, with a mean of 1033 clumps per 2 ◦ × ◦ region in our sample, we can expect nearly 2 × clumps throughout the entire 360 ◦ of Hi-GAL. This is in excellentagreement with Molinari et al. (2016), who found 85,460 clumps in Hi-GAL at 500 µ m between (cid:96) = − ◦ and (cid:96) = 68 ◦ .When extrapolated linearly, this predicts 2 . × clumps in the entire Galactic plane. In those regions where GRSis available, 59% of clumps had some distance information, and 33% had well-constrained distances. Thus, in the full15 ◦ ≤ (cid:96) ≤ ◦ range covered by GRS, we can expect approximately 25,000 clumps with some distance information,and 14,000 clumps with well-constrained distances. We will be including other molecular line surveys in addition toGRS: MALT90 (Jackson et al. 2013), ThrUMMs ( ), andSEDIGISM ( http://colloques.lam.fr/GESF2014/S3/092_SCHULLE_Frederic.pdf ). Once these surveys and thelack of a kinematic distance ambiguity in the outer Galaxy are accounted for, we expect some distance informationfor 50% of clumps and well-constrained distances for 20% of clumps. This corresponds to 100,000 clumps and 40,000clumps, respectively, providing a very large sample for investigating Galactic structure and physical properties ofmolecular cloud clumps.BGPS used very different techniques for data reduction and large-scale structure removal than what we employedfor Hi-GAL. Atmospheric emission was removed directly from the BGPS time-stream. In addition to removing atmo-spheric emission, this acted as an angular filter, allowing only emission on scales less than about 6 (cid:48) — which roughlycorresponded with the array field of view of 7 (cid:48) . (cid:96) = 217 ◦ region correspond well visually with what Eliaet al. (2013) found in their study of the same Hi-GAL region. The brighter filaments match up particularly well, andalthough we find more faint clumps, this is understandable, since their study was concerned only with objects forwhich there was a CO(1–0) velocity measurement. Although we do not yet have distances — and therefore massesand physical radii — for clumps in this outer Galaxy region, it can be noted that the masses and radii for our innerGalaxy clumps span a broader range than those of Elia et al. (2013). We find clumps down to smaller masses, as wellas up to greater masses, and similarly for physical radii.In future work we will extend beyond our sample of six regions and identify clumps in the entirety of Hi-GAL.We will also incorporate other molecular line surveys for the purpose of obtaining kinematic distances for clumpsin areas of the Galactic plane not covered by GRS. Furthermore, we will develop two additional priors. The firstrelies on molecular cloud clumps’ absorption of starlight at short wavelengths and uses that to determine near-infraredextinction (NIREX) distances (Marshall et al. 2009). The second, H I self-absorption (HISA) and H I emission /absorption (HIE/A) techniques, was introduced by Roman-Duval et al. (2009) and Anderson & Bania (2009), andused on ATLASGAL clumps by Wienen et al. (2015). CONCLUSIONS
MC Clump Catalog: Method and Initial Results × clumps, again with 20% having well-constrained distances.This project was supported in part by RSA 1500521 from JPL pursuant to NASA Prime Contract No. NNN12AA01C.SPIRE has been developed by a consortium of institutes led by Cardi University (UK) and including Univ. Lethbridge(Canada); NAOC (China); CEA, LAM (France); IFSI, Univ. Padua (Italy); IAC (Spain); Stockholm Observatory(Sweden); Imperial College London, RAL, UCL-MSSL, UKATC, Univ. Sussex (UK); and Caltech, JPL, NHSC, Univ.Colorado (USA). This development has been supported by national funding agencies: CSA (Canada); NAOC (China);CEA, CNES, CNRS (France); ASI (Italy); MCINN (Spain); SNSB (Sweden); STFC (UK); and NASA (USA).ER is supported by a Discovery Grant from NSERC of Canada.The authors wish to thank Tim Ellsworth-Bowers for helpful discussions.Part of this work based on archival data, software or online services provided by the ASI SCIENCE DATA CENTER(ASDC). Facility : Herschel (SPIRE) REFERENCES
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APPENDIXTable A1 lists the clumps matched between Hi-GAL and BGPS. Included are Galactic longitude and latitude ( (cid:96), b ),integrated flux density ( S , with subscripts denoting wavelength in µ m), angular radius ( θ R ), and heliocentric distance( d (cid:12) ). Due to high-pass filtering, Hi-GAL integrated flux densities and angular radii are attenuated by (20 ± ± Table A1 . Compared Clumps
Hi-GAL BGPS (cid:96) b S θ R d (cid:12) Catalog (cid:96) b S θ R d (cid:12)◦ ( ◦ ) (Jy) ( (cid:48)(cid:48) ) (kpc) ( ◦ ) ( ◦ ) (Jy) ( (cid:48)(cid:48) ) (kpc)11.32 -0.53 22 . ± .
19 95.2 3 . +0 . − . . ± . · · · . +0 . − . . ± .
30 145.9 3 . +0 . − . . ± . · · · · · ·· · · · · · · · · · · · · · · . ± . · · · · · ·· · · · · · · · · · · · · · · . ± .
32 59.4 2 . +0 . − . · · · · · · · · · · · · · · · . ± . · · · · · ·· · · · · · · · · · · · · · · . ± .
14 37.9 · · ·· · · · · · · · · · · · · · · . ± . · · · · · ·· · · · · · · · · · · · · · · . ± . · · · · · · . ± .
24 109.0 · · · . ± .
28 60.3 · · · . ± .
25 140.4 · · · . ± .
13 39.9 · · ·· · · · · · · · · · · · · · · . ± .
16 39.5 · · · . ± .
15 75.3 · · · . ± .
11 18.9 · · · . ± .
25 131.1 · · · . ± .
19 50.0 · · ·· · · · · · · · · · · · · · · . ± . · · · · · ·· · · · · · · · · · · · · · · . ± . · · · · · · . ± .
21 117.0 · · · . ± .
16 58.5 · · · . ± .
18 85.8 3 . +0 . − . . ± .
15 49.5 3 . +0 . − . . ± .
16 64.9 4 . +0 . − . . ± .
28 63.2 4 . +0 . − . . ± .
10 44.5 · · · . ± . · · · · · · . ± . · · · · · · . ± .
18 51.1 · · · . ± .
18 88.2 · · · . ± .
10 17.4 · · · . ± .
10 47.8 · · · . ± . · · · · · · . ± .
24 95.2 · · · . ± .
95 98.8 · · ·· · · · · · · · · · · · · · · . ± .
11 24.6 · · · . ± .
13 66.1 · · · . ± .
14 44.3 · · ·· · · · · · · · · · · · · · · . ± .
14 40.0 · · · . ± .
22 115.5 · · · . ± .
18 66.9 · · ·· · · · · · · · · · · · · · · . ± .
09 18.9 · · · . ± .
15 67.8 · · · . ± .
36 81.9 · · · . ± .
19 58.9 4 . +0 . − . . ± .
23 52.7 4 . +0 . − . Table A1 continued on next page
MC Clump Catalog: Method and Initial Results Table A1 (continued)
Hi-GAL BGPS (cid:96) b S θ R d (cid:12) Catalog (cid:96) b S θ R d (cid:12)◦ ( ◦ ) (Jy) ( (cid:48)(cid:48) ) (kpc) ( ◦ ) ( ◦ ) (Jy) ( (cid:48)(cid:48) ) (kpc) · · · · · · · · · · · · · · · . ± .
61 80.5 4 . +0 . − . . ± .
11 44.3 · · · . ± .
15 43.8 · · ·· · · · · · · · · · · · · · · . ± . · · · · · · . ± .
17 86.0 · · · . ± . · · · · · · . ± .
20 95.0 · · · . ± .
12 39.6 · · ·· · · · · · · · · · · · · · · . ± .
18 50.8 · · ·· · · · · · · · · · · · · · · . ± .
16 45.2 · · ·· · · · · · · · · · · · · · · . ± .
12 30.1 · · · . ± .
15 71.3 13 . +0 . − . . ± . · · · · · · . ± .
10 45.6 · · · . ± .
14 50.6 · · · . ± .
18 99.7 · · · · · · · · · · · · · · · · · · · · · . ± .
17 90.7 · · · . ± . · · · · · ·· · · · · · · · · · · · · · · . ± . · · · · · · . ± .
12 47.2 · · · . ± .
12 15.7 4 . +0 . − . . ± .
10 34.5 · · · . ± . · · · · · · . ± . · · · . +0 . − . . ± .
18 52.8 4 . +0 . − . . ± .
21 117.5 · · · . ± .
11 42.2 · · · . ± .
16 59.9 4 . +0 . − . . ± .
09 21.6 · · · . ± .
10 33.0 · · · · · · · · · · · · · · · · · · · · ·· · · · · · · · · · · · · · · . ± .
79 105.2 4 . +0 . − . . ± .
13 46.8 4 . +0 . − . . ± .
28 58.7 4 . +0 . − . . ± .
13 58.5 · · · . ± .
14 51.0 · · · . ± .
14 60.9 · · · · · · · · · · · · · · · · · · · · · . ± .
16 83.8 · · · . ± .
15 56.9 · · · . ± .
21 107.6 4 . +0 . − . . ± . · · · · · ·· · · · · · · · · · · · · · · . ± .
16 63.3 4 . +0 . − . . ± .
14 59.8 4 . +0 . − . . ± .
26 71.7 4 . +0 . − . . ± .
16 81.5 · · · . ± .
11 26.1 · · · . ± .
14 62.6 · · · . ± .
16 50.2 · · · . ± .
15 84.5 · · · . ± .
14 27.5 · · · . ± .
11 42.6 · · · . ± .
12 36.5 · · · . ± .
22 111.9 · · · . ± . · · · · · ·· · · · · · · · · · · · · · · . ± .
23 57.2 · · ·· · · · · · · · · · · · · · · . ± .
09 18.0 · · · . ± . · · · · · · . ± .
12 22.1 · · · . ± .
13 55.3 · · · . ± .
14 29.1 · · · . ± .
18 69.4 · · · . ± .
17 25.9 · · · . ± .
12 45.9 · · · . ± . · · · · · · . ± . · · · · · · . ± . · · · · · ·· · · · · · · · · · · · · · · . ± . · · · · · · . ± .
18 83.6 · · · . ± .
17 46.7 · · · . ± .
14 49.3 · · · . ± .
25 62.8 · · · . ± .
18 72.7 · · · . ± .
19 49.8 · · · . ± .
14 75.4 · · · . ± .
09 16.1 · · · . ± .
13 58.0 · · · . ± .
21 57.0 · · · . ± .
12 52.5 · · · . ± . · · · · · · . ± .
12 44.3 · · · . ± . · · · · · · . ± .
22 120.2 · · · . ± .
13 38.7 · · ·· · · · · · · · · · · · · · · . ± .
14 51.7 · · ·
Table A1 continued on next page Zetterlund et al.
Table A1 (continued)
Hi-GAL BGPS (cid:96) b S θ R d (cid:12) Catalog (cid:96) b S θ R d (cid:12)◦ ( ◦ ) (Jy) ( (cid:48)(cid:48) ) (kpc) ( ◦ ) ( ◦ ) (Jy) ( (cid:48)(cid:48) ) (kpc) · · · · · · · · · · · · · · · . ± .
12 36.8 · · · . ± .
12 59.4 · · · . ± .
18 61.8 · · · . ± .
12 52.1 3 . +0 . − . . ± .
17 45.5 3 . +0 . − . . ± .
12 41.1 · · · . ± .
25 44.4 · · · . ± .
14 55.1 · · · . ± .
17 43.9 · · ·· · · · · · · · · · · · · · · . ± .
15 31.6 · · · . ± .
13 64.5 · · · . ± .
15 50.0 · · · . ± .
19 86.6 · · · . ± .
16 53.5 · · · . ± .
18 66.0 13 . +0 . − . . ± .
45 65.7 · · · . ± . · · · · · · . ± .
13 40.6 · · · . ± .
14 57.3 · · · . ± .
14 41.8 · · · . ± .
19 98.7 · · · . ± .
13 39.8 · · · . ± .
08 25.1 · · · . ± .
11 32.4 · · · . ± . · · · · · · . ± .
12 10.8 · · · . ± .
14 66.1 2 . +0 . − . . ± .
59 108.9 3 . +0 . − . . ± .
16 73.4 3 . +0 . − . · · · · · · · · · · · · · · · · · · . ± .
15 66.2 12 . +0 . − . . ± .
15 47.3 12 . +0 . − . · · · · · · · · · · · · · · · . ± .
11 22.8 12 . +0 . − . . ± .
12 60.1 3 . +0 . − . . ± .
18 54.4 3 . +0 . − . . ± .
16 77.7 3 . +0 . − . . ± .
20 69.9 3 . +0 . − . . ± .
19 81.9 · · · . ± .
51 63.2 · · ·· · · · · · · · · · · · · · · . ± .
37 53.1 · · ·· · · · · · · · · · · · · · · . ± .
09 8.6 · · · . ± .
10 21.1 · · · . ± . · · · · · · . ± .
16 75.8 · · · . ± . · · · · · · . ± . · · · · · · . ± . · · · · · · . ± .
13 40.9 · · · . ± .
82 53.5 · · · . ± .
10 45.8 · · · . ± .
14 51.1 · · · . ± .
13 38.6 · · · . ± . · · · · · · . ± .
13 46.9 · · · . ± .
14 27.9 · · · . ± . · · · · · · . ± . · · · · · · . ± .
19 109.9 · · · . ± .
22 74.5 · · ·· · · · · · · · · · · · · · · . ± . · · · · · · . ± .
11 45.9 · · · . ± . · · · · · · . ± .
20 104.7 2 . +0 . − . . ± .
37 96.2 3 . +0 . − . . ± .
21 110.8 3 . +0 . − . . ± .
29 78.3 3 . +0 . − . · · · · · · · · · · · · · · · . ± .
14 10.7 3 . +0 . − . . ± .
28 136.4 3 . +0 . − . . ± .
28 70.1 3 . +0 . − . · · · · · · · · · · · · · · · . ± . · · · · · ·· · · · · · · · · · · · · · · . ± .
41 82.1 2 . +0 . − . . ± .
14 56.1 · · · . ± .
14 35.1 · · · . ± .
11 44.6 · · · . ± . · · · · · · . ± . · · · · · · . ± .
10 12.3 · · · . ± .
13 67.3 · · · . ± .
16 58.6 · · · . ± .
18 83.2 · · · . ± . · · · · · ·· · · · · · · · · · · · · · · . ± .
23 74.0 · · · . ± . · · · · · · . ± . · · · · · · . ± .
12 57.6 3 . +0 . − . . ± .
14 28.2 3 . +0 . − . . ± .
14 53.7 · · · . ± .
13 44.5 · · ·
Table A1 continued on next page
MC Clump Catalog: Method and Initial Results Table A1 (continued)
Hi-GAL BGPS (cid:96) b S θ R d (cid:12) Catalog (cid:96) b S θ R d (cid:12)◦ ( ◦ ) (Jy) ( (cid:48)(cid:48) ) (kpc) ( ◦ ) ( ◦ ) (Jy) ( (cid:48)(cid:48) ) (kpc)10.88 -0.05 5 . ± . · · · · · · . ± .
11 36.4 · · · . ± . · · · · · · . ± . · · · · · · . ± .
13 52.7 · · · . ± .
12 24.4 · · · . ± . · · · . +0 . − . . ± .
11 18.8 3 . +0 . − . . ± .
10 43.0 · · · . ± .
40 116.7 · · · . ± .
23 107.5 · · · · · · · · · · · · · · · · · · · · ·· · · · · · · · · · · · · · · . ± . · · · · · · . ± .
16 52.4 · · · . ± .
32 72.8 · · · . ± .
12 43.4 · · · . ± .
12 30.7 · · · . ± .
17 86.3 · · · . ± .
34 82.0 · · · . ± .
18 79.0 9 . +0 . − . . ± .
19 49.2 9 . +0 . − . . ± .
10 41.0 · · · . ± . · · · · · · . ± . · · · · · · . ± . · · · · · · . ± .
19 73.7 · · · . ± .
32 64.3 · · · . ± .
17 72.4 · · · . ± .
20 64.3 · · ·· · · · · · · · · · · · · · · . ± .
09 33.4 · · · . ± .
12 52.0 · · · . ± .
10 20.9 · · · . ± .
13 51.8 · · · . ± .
11 29.6 · · · . ± .
10 42.0 · · · . ± . · · · · · · . ± .
10 25.1 · · · . ± .
14 30.1 · · · . ± .
16 82.7 · · · . ± .
11 23.9 · · · . ± . · · · · · · . ± . · · · · · · . ± .
11 46.1 · · · . ± .
12 33.8 · · · . ± .
19 103.2 · · · . ± . · · · · · ·· · · · · · · · · · · · · · · . ± . · · · · · · . ± .
12 50.7 · · · . ± .
10 29.7 · · ·· · · · · · · · · · · · · · · . ± . · · · · · · . ± .
17 80.7 · · · . ± . · · · · · ·· · · · · · · · · · · · · · · . ± .
30 82.8 · · · . ± .
15 50.6 · · · . ± .
34 59.0 · · · . ± .
18 95.2 · · · . ± . · · · · · ·· · · · · · · · · · · · · · · . ± .
23 77.4 · · · . ± .
09 18.1 · · · . ± . · · · · · ·· · · · · · · · · · · · · · · . ± .
10 17.8 · · · . ± .
10 35.4 · · · . ± . · · · · · · . ± .
16 85.0 · · · . ± .
13 37.0 · · · . ± . · · · · · · . ± . · · · · · · . ± .
10 38.7 · · · . ± . · · · · · · . ± . · · · · · · . ± .
12 35.4 · · ·· · · · · · · · · · · · · · · . ± . · · · · · · . ± .
13 37.0 · · · . ± .
27 55.8 · · · . ± .
17 90.8 · · · . ± .
10 26.9 · · · . ± .
20 102.9 · · · . ± . · · · · · · . ± .
16 80.1 · · · . ± .
12 35.6 · · ·· · · · · · · · · · · · · · · . ± . · · · · · · . ± .
10 29.0 · · · . ± .
20 65.3 · · · . ± .
13 46.4 · · · . ± .
13 18.2 · · · . ± .
09 38.7 · · · . ± .
13 43.7 · · · . ± .
22 116.6 · · · . ± .
27 89.9 2 . +0 . − . Table A1 continued on next page Zetterlund et al.
Table A1 (continued)
Hi-GAL BGPS (cid:96) b S θ R d (cid:12) Catalog (cid:96) b S θ R d (cid:12)◦ ( ◦ ) (Jy) ( (cid:48)(cid:48) ) (kpc) ( ◦ ) ( ◦ ) (Jy) ( (cid:48)(cid:48) ) (kpc)10.92 0.09 2 . ± . · · · · · · . ± . · · · · · ·· · · · · · · · · · · · · · · . ± . · · · · · · . ± .
09 23.6 · · · . ± .
10 25.5 · · ·· · · · · · · · · · · · · · · . ± . · · · · · · . ± . · · · · · · . ± .
09 21.5 · · · . ± .
16 73.5 · · · . ± .
10 37.7 · · · . ± .
19 82.4 · · · . ± .
19 46.6 · · · . ± .
21 121.0 14 . +1 . − . . ± .
20 58.0 · · · . ± .
21 94.9 3 . +0 . − . . ± .
26 70.6 3 . +0 . − . . ± .
20 105.1 · · · . ± .
10 18.9 · · ·· · · · · · · · · · · · · · · . ± .
15 50.7 · · · . ± .
12 42.4 · · · . ± . · · · · · · . ± .
17 77.7 · · · . ± .
09 20.6 · · ·· · · · · · · · · · · · · · · . ± .
11 25.6 · · · . ± .
18 91.5 · · · . ± . · · · · · · . ± . · · · · · · . ± . · · · · · · . ± .
18 86.4 · · · . ± .
18 65.0 · · ·· · · · · · · · · · · · · · · . ± .
08 28.3 · · · . ± .
17 84.8 · · · . ± .
13 53.1 · · · . ± .
21 115.2 · · · . ± .
12 29.5 · · · . ± .
15 73.5 3 . +0 . − . . ± . · · · . +0 . − . . ± .
18 85.1 3 . +0 . − . . ± .
15 40.1 3 . +0 . − . · · · · · · · · · · · · · · · . ± .
12 34.9 3 . +0 . − . . ± .
12 43.6 · · · . ± .
10 20.1 · · · . ± .
12 54.9 · · · . ± .
10 22.8 · · ·· · · · · · · · · · · · · · · . ± . · · · · · · . ± .
08 27.0 2 . +0 . − . . ± .
14 42.7 2 . +0 . − . . ± .
31 203.3 · · · . ± . · · · · · · . ± .
11 58.2 4 . +0 . − . . ± .
30 41.8 4 . +0 . − . . ± .
18 116.5 4 . +0 . − . . ± .
42 51.8 4 . +0 . − . · · · · · · · · · · · · · · · . ± .
34 37.2 4 . +0 . − . . ± .
20 117.6 4 . +0 . − . . ± .
37 34.9 4 . +0 . − . · · · · · · · · · · · · · · · . ± . · · · . +0 . − . . ± .
21 128.4 4 . +0 . − . . ± .
57 83.9 4 . +0 . − . · · · · · · · · · · · · · · · . ± .
18 45.3 4 . +0 . − . . ± .
07 36.5 · · · . ± .
16 53.1 · · · . ± .
10 65.0 · · · · · · · · · · · · · · · · · · · · · . ± .
10 65.3 3 . +0 . − . . ± .
09 29.2 3 . +0 . − . . ± .
08 52.3 2 . +0 . − . . ± .
12 47.8 2 . +0 . − . . ± .
14 95.1 · · · . ± .
12 48.6 · · · . ± .
08 34.6 3 . +0 . − . . ± .
10 22.7 3 . +0 . − . . ± .
07 36.6 · · · . ± .
09 22.9 2 . +0 . − . . ± .
14 93.1 · · · . ± .
12 41.2 · · · . ± .
08 43.5 · · · . ± .
12 46.7 · · · . ± .
17 107.6 4 . +0 . − . . ± .
24 81.4 · · · . ± .
11 73.7 · · · . ± .
10 38.1 · · · . ± . · · · · · · . ± .
13 26.7 · · · . ± .
12 77.8 4 . +0 . − . . ± . · · · · · · Table A1 continued on next page
MC Clump Catalog: Method and Initial Results Table A1 (continued)
Hi-GAL BGPS (cid:96) b S θ R d (cid:12) Catalog (cid:96) b S θ R d (cid:12)◦ ( ◦ ) (Jy) ( (cid:48)(cid:48) ) (kpc) ( ◦ ) ( ◦ ) (Jy) ( (cid:48)(cid:48) ) (kpc) · · · · · · · · · · · · · · · . ± . · · · . +0 . − . . ± .
15 95.6 · · · . ± .
15 62.0 · · ·· · · · · · · · · · · · · · · . ± . · · · · · · . ± .
12 65.6 · · · . ± . · · · · · ·· · · · · · · · · · · · · · · . ± .
14 56.3 · · · . ± .
15 113.7 · · · . ± .
10 42.0 · · ·· · · · · · · · · · · · · · · . ± .
08 18.9 · · ·· · · · · · · · · · · · · · · . ± .
13 44.0 · · ·· · · · · · · · · · · · · · · . ± .
11 39.1 · · · . ± .
10 44.7 · · · . ± . · · · . +0 . − . . ± .
10 57.3 8 . +0 . − . . ± .
15 56.8 8 . +0 . − . . ± .
11 67.9 · · · . ± .
11 37.3 5 . +0 . − . . ± .
10 50.2 8 . +0 . − . . ± .
21 68.2 8 . +0 . − . . ± .
09 57.7 · · · . ± . · · · · · · . ± .
17 121.4 · · · . ± .
19 65.8 5 . +0 . − . . ± .
10 60.3 · · · . ± .
07 13.1 · · ·· · · · · · · · · · · · · · · . ± .
14 57.4 · · · . ± .
09 50.5 · · · . ± .
09 35.1 5 . +0 . − . . ± .
08 40.0 · · · . ± .
10 26.9 5 . +0 . − . . ± .
11 76.2 · · · . ± . · · · · · ·· · · · · · · · · · · · · · · . ± .
14 52.9 · · · . ± .
22 118.0 9 . +0 . − . . ± .
58 91.5 5 . +0 . − . · · · · · · · · · · · · · · · . ± .
15 52.9 · · · . ± . · · · · · · . ± .
10 23.0 · · · . ± . · · · . +0 . − . . ± . · · · · · · . ± .
11 58.3 · · · . ± .
17 57.8 9 . +0 . − . · · · · · · · · · · · · · · · . ± . · · · · · · . ± .
11 64.0 · · · . ± .
15 48.0 · · · . ± .
18 136.1 · · · . ± .
30 118.7 · · ·· · · · · · · · · · · · · · · . ± .
19 75.7 · · · . ± .
17 121.4 · · · . ± .
14 61.1 · · · . ± .
08 60.9 · · · . ± .
34 117.2 5 . +0 . − . . ± .
14 82.3 5 . +0 . − . · · · · · · · · · · · · · · · · · · . ± .
16 110.7 4 . +0 . − . . ± .
10 27.6 4 . +0 . − . · · · · · · · · · · · · · · · . ± .
10 35.4 4 . +0 . − . . ± .
17 87.2 8 . +0 . − . . ± .
91 117.5 8 . +0 . − . . ± .
10 62.8 · · · . ± .
11 41.4 · · · . ± .
10 62.5 · · · . ± .
10 31.7 · · · . ± .
18 124.5 · · · . ± .
11 38.5 7 . +1 . − . · · · · · · · · · · · · · · · . ± .
46 115.3 · · · . ± .
11 68.4 4 . +0 . − . . ± .
18 76.9 · · · . ± .
09 50.6 5 . +0 . − . . ± . · · · · · ·· · · · · · · · · · · · · · · . ± . · · · · · · . ± .
18 102.3 · · · . ± .
81 122.0 · · · . ± .
16 96.1 · · · . ± .
26 81.6 · · · . ± .
11 44.6 · · · . ± . · · · · · · . ± .
09 47.5 · · · . ± .
10 29.1 · · ·· · · · · · · · · · · · · · · . ± .
17 54.9 · · ·
Table A1 continued on next page Zetterlund et al.
Table A1 (continued)
Hi-GAL BGPS (cid:96) b S θ R d (cid:12) Catalog (cid:96) b S θ R d (cid:12)◦ ( ◦ ) (Jy) ( (cid:48)(cid:48) ) (kpc) ( ◦ ) ( ◦ ) (Jy) ( (cid:48)(cid:48) ) (kpc)30.31 -0.15 16 . ± .
10 55.7 6 . +1 . − . . ± .
20 71.2 6 . +1 . − . . ± .
07 35.4 7 . +1 . − . . ± .
23 76.1 7 . +1 . − . . ± .
13 102.9 4 . +0 . − . . ± .
15 74.9 · · ·· · · · · · · · · · · · · · · . ± . · · · · · · . ± .
13 85.3 · · · . ± . · · · · · ·· · · · · · · · · · · · · · · . ± .
27 93.3 · · · . ± .
07 30.3 5 . +0 . − . . ± .
14 44.5 5 . +0 . − . . ± .
12 70.7 · · · . ± .
36 99.7 · · · . ± .
14 90.9 · · · . ± .
39 103.6 7 . +1 . − . . ± .
14 87.0 · · · . ± .
62 102.6 7 . +1 . − . . ± .
10 72.4 · · · . ± .
12 58.5 · · · . ± .
07 27.2 5 . +0 . − . . ± .
18 68.2 5 . +0 . − . . ± . · · · . +0 . − . . ± .
13 37.8 · · · . ± .
08 52.8 · · · . ± .
19 79.8 · · · . ± .
10 60.0 · · · . ± .
11 33.0 · · · . ± .
10 57.9 4 . +0 . − . . ± .
22 68.3 4 . +0 . − . · · · · · · · · · · · · · · · . ± . · · · · · · . ± .
09 49.2 4 . +0 . − . . ± .
16 55.0 · · · . ± .
14 103.1 · · · . ± .
12 38.9 · · · . ± .
10 59.4 5 . +0 . − . . ± .
96 157.1 5 . +0 . − . . ± .
14 85.5 5 . +0 . − . · · · · · · · · · · · · · · · · · · . ± . · · · . +0 . − . . ± .
10 34.9 6 . +1 . − . . ± .
10 43.7 5 . +0 . − . . ± .
74 88.8 5 . +0 . − . . ± .
09 60.8 5 . +0 . − . . ± .
16 40.1 · · · . ± .
08 31.4 5 . +0 . − . . ± .
28 70.2 5 . +0 . − . . ± .
10 54.2 11 . +0 . − . . ± .
26 85.1 11 . +0 . − . . ± .
17 120.3 · · · . ± .
17 76.4 · · · . ± .
07 45.1 · · · . ± . · · · · · ·· · · · · · · · · · · · · · · . ± .
14 52.0 · · · . ± .
13 60.5 5 . +0 . − . . ± .
19 119.3 5 . +0 . − . . ± . · · · . +0 . − . · · · · · · · · · · · · · · · · · ·· · · · · · · · · · · · · · · . ± .
15 34.3 · · · . ± .
13 88.0 · · · . ± .
16 72.6 · · · . ± .
08 51.3 · · · . ± .
13 48.4 · · · . ± .
06 16.2 · · · . ± .
09 23.8 · · · . ± .
11 60.5 · · · . ± .
75 93.0 5 . +0 . − . . ± .
08 31.8 5 . +0 . − . . ± .
30 77.4 5 . +0 . − . . ± .
12 75.7 · · · . ± .
19 70.9 · · · . ± .
09 50.7 · · · . ± .
12 46.1 · · · . ± .
06 21.0 · · · . ± .
12 38.4 · · · . ± .
18 137.8 · · · . ± . · · · · · · . ± .
09 43.0 5 . +0 . − . . ± .
34 80.2 5 . +0 . − . . ± .
10 57.9 · · · . ± .
17 60.6 · · ·· · · · · · · · · · · · · · · . ± . · · · · · · . ± .
09 49.8 · · · . ± .
25 66.7 · · · . ± .
13 77.6 · · · . ± .
31 80.8 · · · . ± . · · · · · · . ± . · · · · · · . ± .
07 36.1 · · · · · · · · · · · · · · · · · · · · ·
Table A1 continued on next page
MC Clump Catalog: Method and Initial Results Table A1 (continued)
Hi-GAL BGPS (cid:96) b S θ R d (cid:12) Catalog (cid:96) b S θ R d (cid:12)◦ ( ◦ ) (Jy) ( (cid:48)(cid:48) ) (kpc) ( ◦ ) ( ◦ ) (Jy) ( (cid:48)(cid:48) ) (kpc) · · · · · · · · · · · · · · · . ± .
20 73.3 · · · . ± .
09 47.7 · · · . ± . · · · · · ·· · · · · · · · · · · · · · · . ± . · · · · · · . ± .
09 46.1 · · · . ± .
08 26.8 · · · . ± .
14 92.2 11 . +0 . − . . ± .
35 95.3 · · · . ± .
08 36.7 · · · . ± .
15 47.9 · · · . ± .
10 64.0 · · · . ± .
14 60.4 · · · . ± . · · · · · · . ± .
12 41.9 · · · . ± .
11 65.1 · · · . ± .
20 66.9 · · · . ± .
15 92.5 · · · . ± .
41 86.8 · · ·· · · · · · · · · · · · · · · . ± .
13 35.2 7 . +1 . − . . ± .
14 89.1 · · · . ± .
33 90.7 · · · . ± .
11 65.6 · · · . ± . · · · · · ·· · · · · · · · · · · · · · · . ± .
15 51.4 · · · . ± .
19 129.6 · · · . ± .
18 76.1 · · ·· · · · · · · · · · · · · · · . ± .
38 106.1 · · · . ± .
11 74.6 · · · . ± . · · · · · · . ± .
12 73.0 16 . +0 . − . . ± . · · · . +0 . − . · · · · · · · · · · · · · · · . ± .
12 39.9 16 . +0 . − . . ± . · · · . +0 . − . . ± .
11 27.3 · · · . ± . · · · · · · . ± . · · · · · · . ± .
16 110.1 · · · . ± .
18 72.7 · · ·· · · · · · · · · · · · · · · . ± .
08 24.2 · · · . ± .
16 105.1 4 . +0 . − . . ± . · · · · · ·· · · · · · · · · · · · · · · . ± .
15 47.5 4 . +0 . − . . ± .
11 63.9 · · · . ± .
12 33.2 · · · . ± .
14 95.5 · · · . ± .
19 80.0 · · · . ± .
11 71.1 · · · . ± .
10 37.2 · · · . ± . · · · · · · . ± .
09 32.8 · · · . ± .
07 31.6 4 . +0 . − . . ± .
10 20.4 · · · . ± .
12 72.8 16 . +1 . − . . ± .
15 51.9 · · · . ± .
11 61.9 · · · . ± . · · · · · ·· · · · · · · · · · · · · · · . ± . · · · · · ·· · · · · · · · · · · · · · · . ± . · · · · · · . ± .
08 45.3 · · · . ± .
09 42.2 · · · . ± .
12 73.0 · · · . ± .
15 51.5 · · · . ± .
13 74.1 4 . +0 . − . . ± .
17 61.0 4 . +0 . − . . ± .
06 17.2 · · · . ± .
09 19.2 · · · . ± .
12 62.2 6 . +0 . − . . ± .
20 66.2 6 . +1 . − . · · · · · · · · · · · · · · · . ± . · · · · · · . ± .
06 10.7 · · · . ± . · · · · · · . ± . · · · · · · . ± . · · · · · · . ± .
10 58.1 · · · . ± . · · · · · · . ± .
09 53.4 · · · . ± .
09 22.9 · · · . ± .
12 78.8 · · · . ± . · · · · · ·· · · · · · · · · · · · · · · . ± .
10 36.5 4 . +0 . − . · · · · · · · · · · · · · · · . ± . · · · · · ·· · · · · · · · · · · · · · · . ± . · · · · · · . ± .
10 62.9 · · · . ± .
11 35.2 · · ·
Table A1 continued on next page Zetterlund et al.
Table A1 (continued)
Hi-GAL BGPS (cid:96) b S θ R d (cid:12) Catalog (cid:96) b S θ R d (cid:12)◦ ( ◦ ) (Jy) ( (cid:48)(cid:48) ) (kpc) ( ◦ ) ( ◦ ) (Jy) ( (cid:48)(cid:48) ) (kpc)30.40 0.37 21 . ± .
13 89.6 6 . +0 . − . . ± . · · · . +1 . − . · · · · · · · · · · · · · · · . ± .
08 32.5 6 . +2 . − . . ± .
10 50.3 · · · . ± .
07 17.3 · · · . ± .
14 83.4 · · · . ± .
14 44.7 4 . +0 . − . . ± .
14 80.3 · · · . ± .
21 62.0 · · · . ± .
09 48.4 · · · . ± .
08 21.7 · · · . ± .
23 152.1 1 . +0 . − . . ± .
38 101.2 1 . +0 . − . . ± .
10 43.7 1 . +0 . − . . ± .
15 23.0 1 . +0 . − . . ± .
08 43.7 1 . +0 . − . . ± .
16 41.6 1 . +0 . − . . ± .
08 44.1 1 . +0 . − . . ± .
12 21.5 1 . +0 . − . . ± .
09 55.2 · · · . ± . · · · · · · . ± .
09 96.3 · · · . ± . · · · · · · . ± .
11 112.5 3 . +0 . − . . ± . · · · · · · . ± .
08 63.4 8 . +0 . − . . ± .
21 39.8 8 . +0 . − . . ± .
08 73.2 6 . +1 . − . . ± . · · · . +1 . − . . ± .
07 52.2 · · · . ± .
25 57.9 · · · . ± .
13 121.1 4 . +0 . − . . ± .
41 80.5 4 . +0 . − . . ± .
07 55.3 · · · . ± .
12 22.2 · · · . ± .
09 105.3 · · · . ± .
24 64.7 · · · . ± .
07 63.4 1 . +0 . − . . ± .
17 37.6 1 . +0 . − . . ± .
07 68.2 8 . +0 . − . . ± .
10 14.4 8 . +0 . − . · · · · · · · · · · · · · · · . ± .
20 52.9 8 . +0 . − . . ± .
13 152.6 · · · . ± .
15 40.8 · · · . ± .
07 77.0 · · · . ± . · · · · · · . ± .
08 84.0 8 . +0 . − . . ± .
29 80.6 8 . +0 . − . . ± .
06 47.2 · · · . ± . · · · · · · . ± . · · · · · · . ± . · · · · · · . ± .
07 69.9 · · · . ± .
18 45.8 · · ·· · · · · · · · · · · · · · · . ± .
10 23.7 · · · . ± .
06 51.5 8 . +0 . − . . ± .
14 38.1 8 . +0 . − . . ± .
05 17.8 · · · . ± . · · · · · ·· · · · · · · · · · · · · · · . ± .
10 26.8 · · · . ± .
11 112.7 · · · . ± .
23 67.7 · · · . ± .
07 63.2 · · · . ± .
18 40.5 3 . +0 . − . . ± .
08 84.0 · · · . ± . · · · · · · . ± .
13 155.2 · · · . ± .
11 28.8 · · · . ± .
05 45.5 · · · . ± . · · · · · · . ± .
06 41.7 · · · . ± . · · · · · · . ± .
10 76.8 · · · . ± .
38 58.5 · · ·· · · · · · · · · · · · · · · . ± .
11 13.6 · · · . ± .
09 87.0 · · · . ± .
18 50.7 · · · . ± .
12 136.9 · · · . ± .
13 21.5 · · · . ± .
07 72.1 · · · . ± .
20 53.8 · · ·· · · · · · · · · · · · · · · . ± . · · · · · ·· · · · · · · · · · · · · · · . ± .
14 40.3 · · · . ± .
11 110.9 · · · . ± .
21 52.6 · · ·· · · · · · · · · · · · · · · . ± . · · · . +1 . − . . ± .
06 54.4 · · · . ± .
12 19.3 · · ·
Table A1 continued on next page
MC Clump Catalog: Method and Initial Results Table A1 (continued)
Hi-GAL BGPS (cid:96) b S θ R d (cid:12) Catalog (cid:96) b S θ R d (cid:12)◦ ( ◦ ) (Jy) ( (cid:48)(cid:48) ) (kpc) ( ◦ ) ( ◦ ) (Jy) ( (cid:48)(cid:48) ) (kpc)41.06 -0.10 6 . ± .
05 33.6 · · · . ± . · · · · · · . ± . · · · · · · . ± . · · · · · · . ± .
10 109.4 9 . +0 . − . . ± .
11 15.7 9 . +0 . − . . ± .
08 78.7 · · · . ± . · · · · · · . ± . · · · · · · . ± .
14 12.8 · · · . ± .
09 90.7 · · · . ± .
16 49.8 · · · . ± .
09 99.6 · · · . ± .
13 35.3 · · · . ± .
08 66.8 3 . +0 . − . . ± .
16 39.5 3 . +0 . − . . ± . · · · · · · . ± . · · · · · · . ± .
08 72.1 11 . +0 . − . . ± .
16 18.5 11 . +0 . − . . ± .
07 66.8 2 . +0 . − . . ± .
14 42.1 · · · . ± .
07 82.1 · · · . ± . · · · · · · . ± .
04 27.7 11 . +0 . − . . ± . · · · . +0 . − . . ± .
07 78.9 · · · . ± . · · · · · ·· · · · · · · · · · · · · · · . ± . · · · · · · . ± .
05 37.7 11 . +0 . − . . ± . · · · · · · . ± .
09 91.4 11 . +0 . − . . ± .
18 40.5 11 . +0 . − . . ± .
06 40.2 15 . +0 . − . . ± . · · · . +0 . − . . ± .
12 127.3 · · · . ± .
13 32.8 · · · . ± .
09 72.8 · · · . ± .
16 31.1 · · · . ± .
11 140.7 4 . +0 . − . . ± . · · · . +0 . − . . ± .
09 91.3 · · · . ± .
15 40.8 · · · . ± .
12 118.6 4 . +0 . − . . ± .
12 20.4 4 . +0 . − . . ± .
07 56.2 11 . +0 . − . . ± . · · · . +0 . − . . ± .
09 83.8 1 . +0 . − . . ± . · · · . +0 . − . . ± .
08 73.7 2 . +0 . − . . ± . · · · · · · . ± .
07 62.2 5 . +0 . − . . ± .
20 22.2 5 . +0 . − . · · · · · · · · · · · · · · · . ± .
29 62.7 · · · . ± .
07 66.7 5 . +0 . − . . ± . · · · . +0 . − . . ± .
07 49.1 5 . +0 . − . . ± .
29 27.6 5 . +0 . − . . ± .
07 65.0 · · · . ± . · · · · · · . ± .
11 119.7 2 . +0 . − . . ± .
33 54.3 2 . +0 . − . . ± .
06 49.8 3 . +1 . − . . ± .
23 34.0 · · · . ± .
08 49.2 · · · . ± .
27 23.3 · · · . ± .
06 22.5 6 . +0 . − . . ± .
01 74.6 6 . +0 . − . . ± .
07 53.7 5 . +1 . − . . ± .
50 72.6 5 . +1 . − . . ± .
07 41.6 5 . +1 . − . . ± .
24 25.7 5 . +1 . − . . ± . · · · · · · . ± . · · · · · ·· · · · · · · · · · · · · · · . ± .
20 43.7 · · · . ± .
08 76.7 7 . +0 . − . . ± .
57 88.0 7 . +0 . − . . ± .
06 44.7 5 . +1 . − . . ± .
19 25.9 5 . +1 . − . . ± . · · · . +0 . − . . ± . · · · . +0 . − . . ± .
12 109.6 · · · . ± .
21 29.3 · · ·· · · · · · · · · · · · · · · . ± .
23 36.3 · · · . ± .
07 71.3 · · · . ± .
20 47.5 · · ·· · · · · · · · · · · · · · · . ± .
20 50.2 · · · . ± .
09 88.0 · · · . ± . · · · · · ·· · · · · · · · · · · · · · · . ± .
17 30.8 · · ·
Table A1 continued on next page Zetterlund et al.
Table A1 (continued)
Hi-GAL BGPS (cid:96) b S θ R d (cid:12) Catalog (cid:96) b S θ R d (cid:12)◦ ( ◦ ) (Jy) ( (cid:48)(cid:48) ) (kpc) ( ◦ ) ( ◦ ) (Jy) ( (cid:48)(cid:48) ) (kpc) · · · · · · · · · · · · · · · . ± .
12 31.0 · · · . ± .
06 63.9 · · · . ± . · · · · · ·· · · · · · · · · · · · · · · . ± . · · · · · · . ± .
10 104.3 5 . +0 . − . . ± . · · · . +1 . − . · · · · · · · · · · · · · · · . ± . · · · . +1 . − . . ± .
13 109.5 5 . +0 . − . . ± .
21 35.4 5 . +0 . − . · · · · · · · · · · · · · · · . ± .
60 79.5 5 . +0 . − . · · · · · · · · · · · · · · · . ± .
39 49.0 5 . +0 . − . . ± .
11 93.3 · · · . ± .
31 37.3 · · · . ± .
12 116.5 5 . +1 . − . . ± . · · · . +1 . − . · · · · · · · · · · · · · · · . ± .
35 42.6 6 . +0 . − . . ± .
08 71.9 · · · . ± . · · · · · · . ± .
08 74.4 · · · . ± . · · · · · · . ± .
10 99.2 5 . +1 . − . . ± .
19 25.8 5 . +1 . − . · · · · · · · · · · · · · · · . ± . · · · . +1 . − . . ± .
11 110.4 · · · . ± . · · · · · · . ± .
08 72.4 12 . +0 . − . . ± .
18 23.8 12 . +0 . − . . ± .
08 57.9 · · · . ± .
20 20.8 · · · . ± .
13 109.4 10 . +0 . − . . ± .
25 31.2 10 . +0 . − . · · · · · · · · · · · · · · · . ± .
15 25.1 10 . +0 . − . . ± .
02 141.9 · · · . ± .
21 24.1 · · ·· · · · · · · · · · · · · · · . ± .
24 34.4 · · · . ± .
01 69.7 · · · . ± . · · · · · · . ± .
01 133.1 · · · . ± . · · · · · ·· · · · · · · · · · · · · · · . ± .
16 22.8 · · · . ± .
01 51.0 · · · . ± .
35 54.7 · · · . ± .
01 55.5 · · · . ± .
26 43.5 · · ·· · · · · · · · · · · · · · · . ± . · · · · · · . ± .
02 95.0 · · · . ± .
23 24.7 · · · . ± .
02 100.3 · · · . ± .
30 30.9 · · · . ± .
01 52.3 · · · . ± .
32 25.4 · · · . ± .
01 63.6 · · · . ± .
31 20.1 · · · . ± .
02 91.7 · · · . ± . · · · · · · . ± .
02 124.9 · · · . ± .
36 45.6 · · · . ± .
02 61.8 · · · . ± .
37 36.1 · · · . ± .
02 83.6 · · · . ± .
16 90.1 · · · . ± .
02 76.9 · · · . ± .
30 29.0 · · · . ± .
02 77.5 · · · . ± .
42 39.2 · · · . ± .
02 62.4 · · · . ± . · · · · · · . ± .
02 122.8 · · · . ± .
38 44.5 · · · aa