Modeling dust emission in the Magellanic Clouds

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Fitting the IR emission in nearby galaxies

Context of this study

Dust plays a fundamental role in the evolution of a galaxy. It has a large impact on the thermo-dynamics and chemistry processes by catalyzing molecular gas formation (e.g. H2 formation sites). It can be a gas tracer when the gas-to-dust ratio is known. It reflects the chemical history of a galaxy… To comprehend the dust impact on other processes and features in the ISM, it is of crucial importance to understand its physical state and composition, including minimal and maximal grain sizes, as described by dust models presented in Section 2.6.
All these models vary from one to another by the definition of dust composition, size dis-tribution of grains, and laboratory-based data for optical properties, and are not necessarily constrained by the same observational references. As described in Chapter 2.7, the widely accepted description of dust involves two main chemical entities: carbonaceous grains, which usually show both amorphous and aromatic structures, and silicate grains, with metallic-element inclusions to agree with the observed abundances.
Section 2.7 presented the progress made in IR observations, from IRAS to Herschel and future JWST. In the ultraviolet, continued observations and analysis of extinction (Cardelli et al. 1988, 1989; Mathis 1990; Fitzpatrick & Massa 2005; Cartledge et al. 2005; Gordon et al. 2003, 2009) and depletions (Jenkins 2009; Tchernyshyov et al. 2015) have shown that large variations in dust properties exist from one line of sight to the next, and between galaxies.
Although we may have identified common behaviour with different models, the same mod-els do not agree on all deduced properties (e.g., dust masses). It is difficult to determine whether the differences between dust studies arise from the intrinsic descriptions of the dust models, or the statistical treatment of the fitting algorithm, or both. In this study published as a paper (Chas-tenet et al. 2017), we use current dust grain models to fit the MIR to sub-millimeter observations of two nearby galaxies. Our goal is to quantitatively measure the discrepancies between the models used in a common fitting scheme, and assess which part of the SEDs can be reproduced best with a given set of physical inputs. To do so, we base our effort on the work of Gordon et al. (2014). In their study, they focused on fitting three models to the Herschel HERITAGE PACS and SPIRE photometric data: the Simple Modified BlackBody, the Broken Emissivity Modified BlackBody and the Two Temperatures Modified BlackBody (SMBB, BEMBB and TTMBB, respectively). They identified a substantial sub-millimeter excess at 500 µ m, in two nearby galaxies, presented below, likely explained by a change in the emissivity slope. They built grids of spectra, varying parameters for a given model (e.g., for the SMBB model, they al-low the dust surface density, the spectral index, and the dust temperature to vary). They adopted a Bayesian approach to derive, for each spectrum, the multi-dimensional likelihood assuming a multi-variate Normal/Gaussian distribution for the data to assess the probability that a set of parameters fit the data. Their residuals and derived gas-to-dust ratio favor the BEMBB model, which best accounts for the sub-millimeter excess. We use the same statistical approach in this study. We present the two galaxies studied here, the Magellanic Clouds, before explaining the data in Section 4.3. Because we extend the observational constraints to shorter wavelengths, we must account for smaller dust grains and “full” models, and we make use of the DustEM tool 1 (Compiègne et al. 2011) to build our own grid of physical dust models (Section 5). We then compare the different models used based on residual characteristics (Section 6) and derive physical properties and interpretations (Sections 7 and 8).

Studying nearby galaxies

The closest galaxy to study is, of course, the one we live in. However, despite obvious high resolution, observing a galaxy from within comes with numerous drawbacks. For instance, the confusion along the line of sight, for any object in the MW lower than a latitude of ∼ 30°, is extremely important. Observations in the galactic disk are very complicated because most of the objects are found in this disk, all mixed together. A similar confusion can be faced when observing external galaxies. Because of our position in the MW, observations of other galaxies may exhibit a foreground, a signal that is not part of the studied object. In the IR, this foreground is the emission of the Galactic cirrus, the atomic gas floating in the MW, and confusing observers. However blaming it all on our Galaxy would be a shame: a confusing background also makes observations difficult. It is the signal emitted by faint and distant galaxies, called the Cosmic Infrared Background. This mixture of signals coming from different parts in space, along a single line-of-sight, cannot be avoided, and only reduced.
Another kind of problematic mixture happens in observations of nearby galaxies. When the spatial resolution is too coarse, the signal contained in that single fraction is the sum of multiple objects in a single pixel. Studying nearby galaxies is a way to decrease the impact of that problem: the closer the galaxy, the finer the spatial resolution, and the lower the number of objects in a single pixel.
Studying these galaxies, however, holds for an argument of statistical sample. Being re-stricted to only our Galaxy does not allow for general theories. The high variety of properties in nearby galaxies is a tremendous advantage in constraining our models: different shapes, dynam-ics, or ages lead to different evolution scenarios, with various galaxy properties. In the case of the ISM, studying galaxies other than the MW gives us access to multiple types of environment in which the dust and gas evolve. Since they are correlated with the star formation and age of a galaxy, we do not expect to see the same properties when it comes to a galaxy different from the MW. Using this different stages helps us to understand the life-cycle of ISM components within the galaxy, and how it changes from one galaxy to the next.
It also allows different approaches to study a galaxy. One can either consider its smaller components: when the resolution is good enough, we can distinguish sub-parts of the galaxy such as bubbles, filaments, star formation regions… These parts have individual properties than (right) galaxies. Credit: NASA – Lorenzo Comolli – Robert Gendler can be of interest as such. On the other hand, one can choose to treat the galaxy as a whole, with average properties. Going further, we might even link the detailed behavior of a galaxy with its global characteristic. In doing so, studying nearby galaxies can be of great help when distant galaxies cannot be resolved.
In general, “nearby galaxies” refers to galaxies within the Local Group. It covers a radius of ∼ 1.5 Mpc around the MW, including about 54 galaxies. It includes three spiral galaxies, represented in Figure 3.1, the Milky Way, the Andromeda Galaxy (M31) and the Triangulum Galaxy (M33). Most of the other objects are irregular and dwarf galaxies, satellites of the Milky Way or Andromeda, including the Magellanic Clouds, two galaxies of particular interest in this thesis.

Description of the Clouds

In the surroundings of the MW, two small galaxies dance around. They can be seen with the naked eye, from the South hemisphere: the Small and Large Magellanic Clouds (together, the MCs; see Figure 4.1); although seen since ancient times, their name is a reference to the navi-gator Ferdinand Magellan who mention them in his travel journal. The Small Magellanic Cloud (SMC) is at 62 kpc from us (Graczyk et al. 2014) while the Large Magellanic Cloud (LMC) stands closer, at about 50 kpc (Keller & Wood 2006; Walker 2012). They are among the closest galaxies to us, classified as dwarf irregular galaxies, and are objects of particular significance for many reasons developed below, when it comes to studying the ISM.
With such proximity to us, the level of details achieved with recent instruments (Spitzer or Herschel) is unprecedented. With the resolution of Herschel, we can resolve down to ∼ 13 pc in the LMC and ∼ 17 pc in the SMC. As a comparison, studies have shown that the typical ISM structure in the dense phase is of a few tens of parsecs (Roman-Duval et al. 2010). The MCs are therefore among the best nearby galaxies to study in terms of resolution. Because of their position (galactic latitude of ∼ 30−45°; ascension and declination coordinates: 00 52 38.0 – 72 48 01 and 05 23 34.6 -69 45 22 for the SMC and LMC, respectively) the MCs suffer from less foreground confusion than other galaxies at lower latitude. Close to the galactic pole, the zodiacal light contamination is also less substantial than in other observations.
With a lower metallicity than the MW, of respectively 1/2 Z⊙ and 1/5 Z⊙ for the LMC and SMC (Russell & Dopita 1992; Rolleston et al. 2002), the MCs are expected to show differences in behaviour than that we would expect in a galaxy similar in metallicity to the MW. With less metals available compared to the MW, the star formation history and composition of dust is bound to be different from what we observe in our Galaxy. In particular, in the scope of this work, the metallicity directly impacts the observed properties of the ISM. The elements available for dust formation depend on the generations of stars that made possible heavy element production, from the nucleosynthesis occurring in their dense cores.
Using H I gas measurements, we find evidence that the two galaxies are in interaction (Put-man et al. 2003; Brüns et al. 2005). The observable track left by this interaction is called the Magellanic Stream (for an extensive review, see D’Onghia & Fox 2016). In the SMC, this stream causes the gas and the stars to behave dynamically different, unlike that usually seen in other, more quiescent galaxies.
In Table 4.1 we gather a few indicators of masses in the galaxies. Despite an order of magnitude of difference in their total dust masses, the SMC atomic gas mass is quite close to that of the LMC. The extent of the H I distribution around the SMC is characteristic of this galaxy: although many galaxies shows a broad distribution, this is more visible in the SMC.

Interest of the MCs

Observations show that the infrared SEDs of the MCs differ from those seen in the Milky Way (MW). At (sub-)millimeter and centimeter wavelengths, dust is well modeled by a blackbody spectrum modified by a power-law (see Section 2.6). Many investigations have identified this trend by pointing out “excess” emission in the far-infrared (FIR) to radio wavelengths (e.g., Galliano et al. 2003, 2005; Bot et al. 2010; Israel et al. 2010; Gordon et al. 2010; Galliano et al. 2011; Gordon et al. 2014). In those models, it means that the spectral emissivity index β is lower in the MCs than in the MW. This excess had also been reported in the MW, although more mildly by Reach et al. (1995), who suggested this excess in the MW comes from cold dust. They rejected this hypothesis as the dust mass needed to account for such an emission (with dust at very low temperature) would be too high to be realistic, and violate elemental abundances. The current theory points toward different a power-law (i.e., different spectral indices) in the expression of the emissivity, in different wavelength ranges (e.g., a ‘broken-emissivity’ modified blackbody model). Another kind of excess has been identified at 70 µ m, with respect to the expected emission from MW-based dust models. The studies of Bot et al. (2004) and Bernard et al. (2008) linked this excess to a different size distribution and abundance of the very small grains whose emission is dominant at these wavelengths. The infrared peak (100 µ m 6 λ 6 250 µ m) also varies between the MW and the MCs and tends to be localized at shorter wavelengths in the SMC. This tendency may be due to the more intense radiation fields in the SMC.
Besides being close and offering good resolution, the MCs therefore seem to be good can-didates to test the limits of our dust models.

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Data used in this study

In this study, I fit the dust emission of the MCs. The MIR, FIR, and sub-millimeter images used in this study are taken from the Spitzer SAGE-SMC (Surveying the Agents of Galaxy Evolu-tion; Gordon et al. 2011) and SAGE-LMC (Meixner et al. 2006) Legacies and the Herschel HERITAGE Key Project (The Herschel Inventory of the Agents of Galaxy Evolution; Meixner et al. 2013, 2015). The SAGE observations were taken with Spitzer Space Telescope (Werner et al. 2004) photometry instruments: the Infrared Array Camera (IRAC; Fazio et al. 2004) pro-vided images at 3.6, 4.5, 5.8 and 8.0 µ m and the Multiband Imaging Photometer for Spitzer (MIPS; Rieke et al. 2004) providing images at 24, 70, and 160 µ m. The observations cover a ∼ 30 °2 region for the SMC and ∼ 50 °2 for the LMC. Data in the FIR to sub-millimeter were taken with PACS (Photoconductor Array Camera and Spectrometer; Poglitsch et al. 2010) and SPIRE (Spectral and Photometric Imaging Receiver; Griffin et al. 2010) on board the Herschel Space Observatory (Pilbratt et al. 2010), providing images at 100, 160, 250, 350, and 500 µ m. The observations cover the same regions as the Spitzer data.
I used the combined Spitzer and Herschel sets of bands to cover the IR spectrum. The combined bands are from IRAC 3.6, 4.5, 5.8, and 8.0 µ m, MIPS 24 and 70 µ m, PACS 100 and 160 µ m, and SPIRE 250, 350 and 500 µ m. Thanks to the custom de-striping techniques used to process the HERITAGE data (see Meixner et al. 2013, for details), the PACS 100 data combines the resolution of Herschel with the sensitivity of IRAS 100. Similarly, the PACS 160 image was merged with the MIPS 160 image.
Like Gordon et al. (2014), first, all the images were convolved using the Aniano et al. (2011) kernels to decrease the spatial resolution of all images to the resolution of the SPIRE 500 µ m band of ∼ 36′′. Next, the foreground dust Milky Way dust emission was subtracted. To do so, we built a MW dust foreground map using the MW velocity H I gas maps from Stanimirovic et al. (2000) for the SMC and Staveley-Smith et al. (2003) for the LMC. To convert the velocity gas maps to a dust emission map, I used the Compiègne et al. (2011) model. I derived conversion coefficients from H I column to MW dust emission, and subtracted the resulting maps from the data.
After this processing, the PACS observations show a gradient across the images. I removed this gradient by subtracting a two-dimensional surface, estimated from background regions in the images. Regions outside the galaxies (and bright sources) were chosen to evaluate a “back-ground” plane that was then subtracted from all the images. For the LMC, the observations did not extend beyond the full disk and this introduced a larger uncertainty in the final background subtracted images. The SMC observations extend beyond the galaxy and we have access to regions on the images fully outside the galaxy. Finally, I rebinned the images to have a pixel scale of ∼ 56′′ that is larger than the resolution of the SPIRE 500 µ m band to provide nominally independent measurements for later fitting. Figures 4.2 and 4.3 shows the final data used in the 11 bands.
To create the model SEDs, I used DustEM. To find the best fit in each pixel, or compare models to observations, I used the SED fitting tool: DustBFF. We will overview here in more details both tools and the work done.

DustEM

The DustEM tool (Compiègne et al. 2011) outputs emission and extinction curves calculated from dust grains properties. For each grain type, properties like the scattering and absorption efficiencies Qext(λ , a) and Qsca(λ , a), and the heat capacities C(λ , a) are used. Each model available explicitly specifies the minimal and maximal grain size amin, amax and the size dis-tribution law to adopt for each grain component. Finally, astronomical data like the full ISRF spectrum is used as well. I use the DustEM IDL wrapper1 to generate full model grids with a large number of emission spectra. The wrapper forward-models the observations by multiply-ing the model SED with transmission curves. I used two dust models in this study (see 2.6 for more details) based on the work from Compiègne et al. (2011) and Jones et al. (2013) updated by Köhler et al. (2014) and Ysard et al. (2015). Here, we remind briefly the reader what the components of these models are.
The model from Compiègne et al. (2011) (MC11) is a mixture of PAHs, both neutral and ionized (cations), small and large amorphous carbonaceous grains (SamC and LamC, respec-tively; Zubko et al. 1996) with different size distributions, and amorphous silicate grains (aSil; Draine & Lee 1984), that is, a total of five independent components. In my fitting, I chose to use only a single PAH population, by summing the ionized and neutral species together. Given the shape of the emission spectra from the charged and neutral PAHs, the broad-band observations could not constrain them independently. I also tied (by summing) the big grains (BGs) together, originally described by both large carbonaceous and amorphous silicates. At λ > 250 µ m, the emissivity law of both carbon and silicate grains in this model is the same (β ∼ 1.7 − 1.8). Hence, they cannot be discriminated from their emission alone and allowing them to vary would result in the fitting arbitrarily choosing one or the other type of grains. Their variations with the temperature are not different enough to be helpful in breaking the degeneracy. More precisely, I use three independent grain populations for this model.
The second model I used is the one for the diffuse-ISM-type dust in the Heterogeneous Evolution Dust Model at the IAS (THEMIS; Jones et al. 2013; Köhler et al. 2014). In this model, the dust is described by two components, split into four populations: very small grains made of aromatic-rich amorphous carbon, large(r) carbonaceous grains with an aliphatic-rich core and an aromatic-rich mantle, and amorphous silicate grains with nano-inclusion of Fe/FeS and aromatic-rich amorphous carbon mantle. The silicate grains are split into two populations: pyroxene (−(SiO3)2) and olivine (−(SiO4)). I choose to tie these two silicate populations for the same reason as previously mentioned: up to 500 µ m, they cannot be discriminated by their emission only. I therefore use three independent grain populations for THEMIS.
There is no clear correspondence between the two models because of their different (yet sometimes overlapping) grain-type definitions. The PAHs are only a feature of the MC11 model, the SamC refers to the small-amorphous carbon grains, and BGs refer to the large-amorphous carbon grains and amorphous silicates. In THEMIS, sCM20 and lCM20 refers to the small- and large- amorphous carbon grains, respectively, and we refer to the pyroxene (aPyM5) and olivine (aOlM5) grains altogether as aSilM5. Figure 5.1 shows the models as they were used with their respective grain populations.
The free parameters we allow to vary in the fitting are YPAHs, YSamC , and YBGs in the MC11 model, and YsCM20, YlCM20, and YaSilM5 in THEMIS. The Yi are scaling factors of the solar neighborhood abundances Mi/MH, where i is one of the grain species (e.g., Compiègne et al. 2011). The SEDs are scaled through these parameters. Additionally, the ISRF environment
will change with different approaches. This is explained in Section 6. Finally, due to short wavelengths and a non-negligible emission from stars in the IRAC bands, I also add a stellar component modeled as a black-body spectrum at 5 000 K. This parameter is scaled through a stellar density Ω∗.

Table of contents :

I General Introduction 
1 Enter the void 
1.1 Watching a galaxy
1.2 The Interstellar Medium
1.3 The Interstellar Radiation Field
2 Interstellar Dust 
2.1 Discovery, history and context
2.2 Dust extinction
2.2.1 Some definitions
2.2.2 Dust physics properties
2.2.3 Measurements of dust extinction
2.2.4 The Diffuse Interstellar Bands
2.3 Dust emission
2.3.1 Thermal equilibrium
2.3.2 Stochastic heating
2.3.3 Aromatic-rich (cyclic) carbonaceous
2.4 Elemental abundances and dust composition
2.5 Grain sizes
2.6 Dust grain models
2.6.1 Draine & Li (2007)
2.6.2 Compiègne et al. (2011)
2.6.3 THEMIS
2.6.4 Calibration
2.7 Observations and Instruments
II Modeling dust emission in the Magellanic Clouds 
3 Fitting the IR emission in nearby galaxies 
3.1 Context of this study
3.2 Studying nearby galaxies
4 The Magellanic Clouds: close neighbors 
4.1 Description of the Clouds
4.2 Interest of the MCs
4.3 Data used in this study
5 Tools and computation 
5.1 DustEM
5.2 DustBFF
5.3 Model (re-)calibration
6 Model comparison 
6.1 Using a single ISRF
6.2 Using multiple ISRFs
6.3 Varying the small grain size distribution
7 Dust properties inferred from modeling 
7.1 Parameter spatial variations
7.2 Silicate grains abundance
7.3 Dust masses and gas-to-dust ratios
8 Exploring the impact of inferred dust properties
8.1 Grain formation/destruction
8.2 Extinction curves
8.3 Other variations in dust models
8.3.1 Change in carbon size distribution
8.3.2 Allowing smaller silicate grains
8.3.3 On the recalibration
8.4 Impact of the ISRF shape
8.5 Using Draine & Li (2007)
9 Conclusions and perspectives on dust in the Magellanic Clouds
III Systematics in Dust Modeling 
10 Using radiative transfer in dust studies
10.1 The Radiative Transfer method
10.2 The Radiative Transfer Equation
10.3 Finding a way to solve
10.3.1 3D Discretization
10.3.2 Make the photons move
10.3.3 Monte Carlo solution
11 The DIRTYGrid
11.1 DIRTYGrid description
11.2 Public distribution
12 Methodology 
12.1 The fitted: SEDs from the DIRTYGrid
12.2 The fitter: full dust model
12.2.1 Draine & Li (2007)
12.2.2 THEMIS
12.2.3 Model Calibration
12.3 Fitting technique
13 Fitting results 
13.1 Using an identical model
13.1.1 Quality of the fits
13.1.2 Recovering dust masses
13.1.3 Finding the PAH Fraction
13.1.4 Investigating the parameter ranges
13.2 Using a different dust composition
13.3 More DIRTYGrid variations
13.3.1 Continuous vs Burst Star formation
13.3.2 Clumpy vs Homogeneous dust distribution
14 Dust RT: conclusions and perspectives
IV General Conclusion & Perspectives 
V Annexes

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