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Register a new userBayesian inference of three-dimensional gas maps: I. Galactic CO
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Carbon monoxide (CO) is the best tracer of Galactic molecular hydrogen (H2). Its lowest rotational emission lines are in the radio regime and thanks to Galactic rotation emission at different distances is Doppler shifted. For a given gas flow model the observed spectra can thus be deprojected along the line of sight to infer the gas distribution. We use the CO line survey of Dame et al. (2001) to reconstruct the three-dimensional density of H2. We consider the deprojection as a Bayesian variational inference problem. The posterior distribution of the gas densities allows us to estimate both the mean and uncertainty of the reconstructed density. Unlike most of the previous attempts, we take into account the correlations of gas on a variety of scales which allows curing some of the well-known pathologies, like fingers-of-god effects. Both gas flow models that we adopt incorporate a Galactic bar which induces radial motions in the inner few kiloparsecs and thus offers spectral resolution towards the Galactic centre. We compare our gas maps with those of earlier studies and characterise their statistical properties, e.g. the radial profile of the average surface mass density. We have made our three-dimensional gas maps and their uncertainties available to the community at https://dx.doi.org/10.5281/zenodo.4405437 .
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We present new three-dimensional (3D) interstellar dust reddening maps of the Galactic plane in three colours, E(G-Ks), E(Bp-Rp) and E(H-Ks). The maps have a spatial angular resolution of 6 arcmin and covers over 7000 deg$^2$ of the Galactic plane for Galactic longitude 0 deg $<$ $l$ $<$ 360 deg and latitude $|b|$ $<$ $10$ deg. The maps are constructed from robust parallax estimates from the Gaia Data Release 2 (Gaia DR2) combined with the high-quality optical photometry from the Gaia DR2 and the infrared photometry from the 2MASS and WISE surveys. We estimate the colour excesses, E(G-Ks), E(Bp-Rp) and E(H-Ks), of over 56 million stars with the machine learning algorithm Random Forest regression, using a training data set constructed from the large-scale spectroscopic surveys LAMOST, SEGUE and APOGEE. The results reveal the large-scale dust distribution in the Galactic disk, showing a number of features consistent with the earlier studies. The Galactic dust disk is clearly warped and show complex structures possibly spatially associated with the Sagittarius, Local and Perseus arms. We also provide the empirical extinction coefficients for the Gaia photometry that can be used to convert the colour excesses presented here to the line-of-sight extinction values in the Gaia photometric bands.
Aims. We investigate the spatial distribution of a collection of absorbing gas clouds, some associated with the dense, massive star-forming core NGC6334 I, and others with diffuse foreground clouds. For the former category, we aim to study the dynamical properties of the clouds in order to assess their potential to feed the accreting protostellar cores. Methods. We use spectral imaging from the Herschel SPIRE iFTS to construct a map of HF absorption at 243 micron in a 6x3.5 arcmin region surrounding NGC6334 I and I(N). Results. The combination of new, spatially fully sampled, but spectrally unresolved mapping with a previous, single-pointing, spectrally resolved HF signature yields a 3D picture of absorbing gas clouds in the direction of NGC6334. Toward core I, the HF equivalent width matches that of the spectrally resolved observation. The distribution of HF absorption is consistent with three of the seven components being associated with this dense star-forming envelope. For two of the remaining four components, our data suggest that these clouds are spatially associated with the larger scale filamentary star-forming complex. Our data also implies a lack of gas phase HF in the envelope of core I(N). Using a simple description of adsorption onto and desorption from dust grain surfaces, we show that the overall lower temperature of the envelope of source I(N) is consistent with freeze-out of HF, while it remains in the gas phase in source I. Conclusions. We use the HF molecule as a tracer of column density in diffuse gas (n(H) ~ 10^2 - 10^3 cm^-3), and find that it may uniquely trace a relatively low density portion of the gas reservoir available for star formation that otherwise escapes detection. At higher densities prevailing in protostellar envelopes (>10^4 cm^-3), we find evidence of HF depletion from the gas phase under sufficiently cold conditions.
Context. While Gaia enables to probe in great detail the extended local neighbourhood, the thin disk structure at larger distances remains sparsely explored. Aims. We aim here to build a non-parametric 3D model of the thin disc structures handling both the extinction and the stellar density simultaneously. Methods. We developed a Bayesian deconvolution method in two dimensions: extinction and distance. It uses a reference catalogue which completeness information defines the selection function. It is designed so that any complementary information from other catalogues can be added. It has also been designed to be robust to outliers, frequent in crowded fields, and differential extinction. The prior information is designed to be minimal: only a reference H-R diagram. We derived for this an empirical H-R diagram of the thin disk using Gaia DR2 data and synthetic isochrone-based H-R diagrams can also be used. Results. We validated the method on simulations and real fields using 2MASS and UKIDSS data complemented by Gaia DR2 photometry and parallaxes. We detail the results of two test fields: a 2MASS field centred around the NGC 4815 open cluster which shows an over-density of both extinction and stellar density at the cluster distance, and a UKIDSS field at $l=10^{circ}$ where we recover the position of the Galactic bar.
We present the first statistical study on the intrinsic three-dimensional (3D) shape of a sample of 83 galactic bars extracted from the CALIFA survey. We use the galaXYZ code to derive the bar intrinsic shape with a statistical approach. The method uses only the geometric information (ellipticities and position angles) of bars and discs obtained from a multi-component photometric decomposition of the galaxy surface-brightness distributions. We find that bars are predominantly prolate-triaxial ellipsoids (68%), with a small fraction of oblate-triaxial ellipsoids (32%). The typical flattening (intrinsic C/A semiaxis ratio) of the bars in our sample is 0.34, which matches well the typical intrinsic flattening of stellar discs at these galaxy masses. We demonstrate that, for prolate-triaxial bars, the intrinsic shape of bars depends on the galaxy Hubble type and stellar mass (bars in massive S0 galaxies are thicker and more circular than those in less massive spirals). The bar intrinsic shape correlates with bulge, disc, and bar parameters. In particular with the bulge-to-total (B/T) luminosity ratio, disc g-r color, and central surface brightness of the bar, confirming the tight link between bars and their host galaxies. Combining the probability distributions of the intrinsic shape of bulges and bars in our sample we show that 52% (16%) of bulges are thicker (flatter) than the surrounding bar at 1$sigma$ level. We suggest that these percentages might be representative of the fraction of classical and disc-like bulges in our sample, respectively.
Star formation has long been known to be an inefficient process, in the sense that only a small fraction $epsilon_{rm ff}$ of the mass of any given gas cloud is converted to stars per cloud free-fall time. However, developing a successful theory of star formation will require measurements of both the mean value of $epsilon_{rm ff}$ and its scatter from one molecular cloud to another. Because $epsilon_{rm ff}$ is measured relative to the free-fall time, such measurements require accurate determinations of cloud volume densities. Efforts to measure the volume density from two-dimensional projected data, however, have thus far relied on treating molecular clouds as simple uniform spheres, while their real shapes are likely filamentary and their density distributions far from uniform. The resulting uncertainty in the true volume density is likely one of the major sources of error in observational estimates of $epsilon_{rm ff}$. In this paper, we use a suite of simulations of turbulent, magnetized, radiative, self-gravitating star-forming clouds to examine whether it is possible to obtain more accurate volume density estimates and thereby reduce this error. We create mock observations from simulations, and show that current analysis methods relying on the spherical assumption likely yield ~ 0.26 dex underestimations and ~ 0.51 dex errors in volume density estimates, corresponding to a ~ 0.13 dex overestimation and a ~ 0.25 dex scatter in $epsilon_{rm ff}$, comparable to the scatter in observed cloud samples. We build a predictive model that uses information accessible in two-dimensional measurements -- most significantly the Gini coefficient of the surface density distribution -- to estimate volume density with ~ 0.3 dex less scatter. We test our method on a recent observation of the Ophiuchus cloud, and show that it successfully reduces the $epsilon_{rm ff}$ scatter.
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