Distances

10¹ 10² 10³

Wavelengthλ[µm]

10⁻¹ 10⁰ 10¹ 10² 10³ 10⁴ 10⁵

FluxdensitySν[Jy]

AGAL050.062+00.062

10¹ 10² 10³

Wavelengthλ[µm]

10⁻¹ 10⁰ 10¹ 10² 10³ 10⁴ 10⁵

FluxdensitySν[Jy]

AGAL305.209+00.206

Figure 2.3: Two example SEDs showing the extracted flux densities fitted with a single-component greybody (left) and a two-single-component model (right). Flux densities are indicated by black crosses, with their uncertainties indicated by vertical bars. In the left panel, the grey-body fit to the flux densities is shown in blue. In the right panel, the blue curve indicates the combined fits of the simultaneously fitted grey- and blackbody models, which are shown by the green dashed curves.

tributes to the flux. In addition this band is in general considered to be contaminated by the emission from small grains (Compiégne 2010,Compiègne et al. 2010), likely resulting in an overestimation of the flux, which was not taken into account in our earlier work. This became obvious when comparing our results to recent work by Guzmán et al. (2015) (see Chapter4 resulting in an over-estimation of the temperatures if a hot component is present. Therefore we adjusted our process, always taking the flux in the 70µm band as an upper limit for the full ATLASGAL catalogue and the outer Galaxy analysis.

In Fig2.3 we show the fitted SEDs for two ATLASGAL sources. In the left panel we present a source only showing emission in the far-infrared to sub-millimetre regime, fitting only the cold-component with the greybody. In the right panel we present a much more evolved and luminous source, with a strong hot component that is fitted by the blackbody in combi-nation with the greybody emission which is simultaneously fitted by the greybody. From the fitted SEDs, we most importantly obtained the cold components temperature representative of the average dust temperature of the clump as well as the luminosity which is obtained from the integral under the fitted model. In combination with the distances, these parameters were consecutively used to calculate the physical properties of the ATLASGAL CSC, building the basis for the discussion and findings of this thesis.

2.4 Distances

Distances within the Milky Way can be obtained through several different methods, with par-allax measurements being the most direct and precise method (Reid & Honma 2014) and inferring distances from a rotation model of the Galaxy by measuring the line-of-sight veloc-ities being the most commonly used method (Urquhart et al. 2018). There are several other

V_{lsr 1}

V_{orbit 1}

V_{lsr 1} V_{orbit 1}

V_{orbit 2}

V_{lsr 2}

Galactic Centre Sun

near

far r₁

r₂

Figure 2.4: Schematic view of rotation around the Galactic centre. There are two solutions for the line-of-sight velocityv_lsr1 when inside the solar circle (green dashed line) as the line of sight crosses the orbit at the near and far distance. No such ambiguity is found for sources located outside the solar circle.

methods to obtain distances especially to evolved stars (e.g. spectroscopic distance measure-ment or distances inferred from the light curves of variable stars or supernova), but as these require the stars to be no longer embedded, these do not play a role in the present work as they are not applicable for the early stages of star formation.

But as the sensitivity of the very long baseline interferometry (VLBI) involved in de-termining parallax distances is rather low (considering the small beam size), this method is only applicable to the brightest objects, preferably masers, at large distances. Furthermore, as parallax measurements require several high precision astrometric measurements at micro-arcsecond resolution distributed over at least one year to measure the parallax to sufficient precision, these measurements can not be obtained through very long baseline interferometry (VLBI) for a huge number of sources in a short amount of time.

In contrast, line-of-sight velocities can be determined within minutes for a single target or inferred for thousands of sources from spectra extracted from CO surveys like SEDIGISM (Schuller et al. 2017). For these reasons, the bulk of the distances to dust clumps in large samples are inferred from line-of-sight velocities using a rotation model of the Galaxy.

In Fig.2.4we show a schematic view of the rotation of the Milky Way. The orbital velocity v_orbitat a given radiusrcan be broken down into two components: one along the line-of-sight and one perpendicular to it. The velocity along the line-of-sight can be directly determined from the Doppler-shift measured for the observed frequency, yielding the velocity measured against the local standard of restv_lsr. As the orbital velocityv_orbitfor a given orbit around the Galactic centre is known from rotation curves measured for the Milky Way, converting the

2.4. DISTANCES 23 observed velocity to a distance is straight forward.

Unfortunately, we can not always convert a Galactocentric distance directly to a heliocen-tric distance as there are always two solutions when the source is located in inner Galaxy (i.e.

R_gal<8.34 kpc): one that is closer to us and another that is on the other side of the Galaxy.

To infer a heliocentric distance we therefore need another indicator if the source is located at the near- or far-distance. For the compact sources of ATLASGAL HIspectra were used to do so, as emission from a source farther away is more likely to show absorption from the medium located between the observer and the source (seeUrquhart et al. 2018; for details). Therefore, for the inner Milky Way, the near-far-distance-ambiguity can be broken by taking into account the HI absorption towards a source of interest.

Although distances inferred from line-of-sight velocity measurements are rather quick and easy to obtain they come with a couple of caveats.

First, the rotation curve of the Galaxy itself is suffering from uncertainties as it is based on secondary distance measurements. Depending on the basis on which a rotation curve is derived, one or another might be preferable. In recent years, the model derived byReid et al.

(2014) is considered the most precise one, as it is inferred from parallax distance measure-ments. But as the measurements only took into account targets visible from the northern hemisphere, we consider the rotation curve fromBrand & Blitz(1993) to be more reliable for sources located in the southern outer Galaxy, as it was derived from observations towards the same region of the sky.

Second, the line-of-sight velocity measurements are not only subject to the Galactic rota-tion, but e.g. also subject to streaming motions, supernovae explosions, or turbulence. These peculiar motions in general add an uncertainty in the order of ∼ ±7−10 km s⁻¹ with re-gard to the Galactic rotation. Thus the relative uncertainty of the distance obtained from a Galactic rotation model is inversely proportional to the distance itself. As a consequence, lo-cal velocity measurements are dominated by the lolo-cal streaming motions, whereas distances for sources located farther away are dominated by the Galactic rotation. This leads to the commonly used rule-of-thumb that distances derived from line-of-sight velocities are accurate within∼ ±1 kpc.

2.4.1 From molecular line observations to distances

As already described in Chapter1.4.3, CO is the second most abundant molecule in the Milky Way after H2. It is therefore widely found in molecular clouds, and even more so in the denser parts constituting the dusty clumps we are investigating in this work. Observing the CO emission towards a dust clump therefore allows us to obtain a velocity for a given clump and from this infer a distance to the clump.

For ATLASGAL and the outer Galaxy we used the facility receiver APEX-1 (SHeFI; Vas-silev et al. 2008) at the Atacama Pathfinder Experiment telescope (APEX; Güsten et al. 2006) to obtain line-of-sight velocities (vlsr) towards the selected sources. For ATLASGAL we aimed at the¹³CO(2–1) and C¹⁸(2–1) transitions at 219.6 and 220.4 GHz, respectively, as these are considered to be more optically thin when compared to the ¹²CO(2–1) transition and there-fore suffer less from line-of-sight confusion. For the outer Galaxy we targeted the¹²CO(2–1) transition at 230.5 GHz, as confusion is not as much an issue for the outer Galaxy than for the

inner Galaxy, and the higher column densities allow for detection of sources at greater dis-tances with similar integration times. Two wide-band Fast Fourier Transform Spectrometers (FFTS; Klein et al. 2012) make up the back-ends, each consisting of 32,768 spectral channels covering an instantaneous bandwidth of 2.5 GHz. With this setup we were able to observe the targeted transitions at a velocity resolution of ∼0.1 km s⁻¹. (see Chapters4.2and5.1.2for observational details).

2.4.1.1 Identifying velocity components

The obtained spectra were reduced using the Continuum and Line Analysis Single-dish Soft-ware (CLASS³). To obtain the velocity components from the observed spectra we first com-bined all scans for a single observed position into a single spectrum. This was smoothed to a velocity resolution of 1 km s⁻¹ and a linear baseline was subtracted. The spectra were then transferred into a Python code, where they were limited to a velocity range of ei-ther −200 km s⁻¹ < vlsr < 200 km s⁻¹ for the ATLASGAL sources or −20 km s⁻¹ < vlsr <

150 km s⁻¹ for the outer Galaxy. Coherent groups of emission and absorption likely associ-ated with a single cloud were determined by defining a window where all emission is above the 3σ noise level. These emission groups are then fitted iteratively, starting with the brightest group. First the spectrum is de-spiked, after which the number of peaks in a group is deter-mined and Gaussian profiles are fitted to the peaks, and the resulting fit is subtracted from the original spectrum. The procedure is then repeated until all groups are fitted, i.e. when no residual above 3σ is found. In order to avoid adding too many emission components that are close to each other and are likely associated with the same cloud to be added, we only considered peaks separated by at least twice the width of the fitted Gaussian as major emission components. The same process was repeated for negative emission features, allowing us to identify observations with a contaminated off-position.

In Fig.2.5we show example spectra of velocity measurements obtained towards sources located in the outer Galaxy. In the upper left panel a single emission component along the line-of-sight can be fitted with a single Gaussian profile. This allows the velocity to be immediately assigned to the dust clump without further analysis.

The situation becomes more difficult when a cloud is either composed of multiple compo-nents or there are multiple clouds located along the line of sight (Fig.2.5, upper right panel).

Furthermore, as CO is the second most abundant molecule in the Milky Way, the reference position used when taking the spectrum might be contaminated, resulting in negative features in the spectrum (Fig.2.5, lower left panel). Contaminated spectra pose the problem that the emission might be at a similar velocity as the negative feature, thus rendering the velocities less reliable. In extreme cases all these three effects are present in a single spectrum taken (lower right panel).

Although these effects complicate the analysis, in most cases still a velocity can be as-signed to the corresponding dust clump. Assuming that the dust emission as seen in the contin-uum maps is associated mainly with the brightest emission found in CO, only the CO emission with the highest integrated intensity is taken into account. If this CO cloud has an integrated intensity at least twice as high as the rest of the CO emission, we assume that the dust emission

3GILDAS/CLASS:https://www.iram.fr/IRAMFR/GILDAS/