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Register a new userFull SED fitting with the KOSMA-tau PDR code - I. Dust modelling
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We revised the treatment of interstellar dust in the KOSMA-tau PDR model code to achieve a consistent description of the dust-related physics in the code. The detailed knowledge of the dust properties is then used to compute the dust continuum emission together with the line emission of chemical species. We coupled the KOSMA-tau PDR code with the MCDRT (multi component dust radiative transfer) code to solve the frequency-dependent radiative transfer equations and the thermal balance equation in a dusty clump under the assumption of spherical symmetry, assuming thermal equilibrium in calculating the dust temperatures, neglecting non-equilibrium effects. We updated the calculation of the photoelectric heating and extended the parametrization range for the photoelectric heating toward high densities and UV fields. We revised the computation of the H2 formation on grain surfaces to include the Eley-Rideal effect, thus allowing for high-temperature H2 formation. We demonstrate how the different optical properties, temperatures, and heating and cooling capabilities of the grains influence the physical and chemical structure of a model cloud. The most influential modification is the treatment of H2 formation on grain surfaces that allows for chemisorption. This increases the total H2 formation significantly and the connected H2 formation heating provides a profound heating contribution in the outer layers of the model clumps. The contribution of PAH surfaces to the photoelectric heating and H2 formation provides a boost to the temperature of outer cloud layers, which is clearly traced by high-J CO lines. Increasing the fraction of small grains in the dust size distribution results in hotter gas in the outer cloud layers caused by more efficient heating and cooler cloud centers, which is in turn caused by the more efficient FUV extinction.
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Modelling and interpreting the SEDs of galaxies has become one of the key tools at the disposal of extragalactic astronomers. Ideally, we could hope that, through a detailed study of its SED, we can infer the correct physical properties and the evolutionary history of a galaxy. In the past decade, panchromatic SED fitting, i.e. modelling the SED over the entire UV-submm wavelength regime, has seen an enormous advance. Several advanced new codes have been developed, nearly all based on Bayesian inference modelling. In this review, we briefly touch upon the different ingredients necessary for panchromatic SED modelling, and discuss the methodology and some important aspects of Bayesian SED modelling. The current uncertainties and limitations of panchromatic SED modelling are discussed, and we explore some avenues how the models and techniques can potentially be improved in the near future.
Recent Herschel and ALMA observations of Photodissociation Regions (PDRs) have revealed the presence of a high thermal pressure (P ~ 10^7-10^8 K cm-3) thin compressed layer at the PDR surface where warm molecular tracer emission (e.g. CH+, SH+, high-J CO, H2,...) originate. These high pressures (unbalanced by the surrounding environment) and a correlation between pressure and incident FUV field (G0) seem to indicate a dynamical origin with the radiation field playing an important role in driving the dynamics. We investigate whether photoevaporation of the illuminated edge of a molecular cloud could explain these high pressures and pressure-UV field correlation. We developed a 1D hydrodynamical PDR code coupling hydrodynamics, EUV and FUV radiative transfer and time-dependent thermo-chemical evolution. We applied it to a 1D plane-parallel photoevaporation scenario where a UV-illuminated molecular cloud can freely evaporate in a surrounding low-pressure medium. We find that photoevaporation can produce high thermal pressures and the observed P-G0 correlation, almost independently from the initial gas density. In addition, we find that constant-pressure PDR models are a better approximation to the structure of photoevaporating PDRs than constant-density PDR models, although moderate pressure gradients are present. Strong density gradients from the molecular to the neutral atomic region are found, which naturally explain the large density contrasts (1-2 orders of magnitude) derived from observations of different tracers. The photoevaporating PDR is preceded by a low velocity shock (a few km/s) propagating into the molecular cloud. Photoevaporating PDR models offer a promising explanation to the recent observational evidence of dynamical effects in PDRs.
We use the SPIRE Fourier-Transform Spectrometer (FTS) on-board the ESA Herschel Space Telescope to analyse the submillimetre spectrum of the Ultra-compact HII region G29.96-0.02. Spectral lines from species including 13CO, CO, [CI], and [NII] are detected. A sparse map of the [NII] emission shows at least one other HII region neighbouring the clump containing the UCHII. The FTS spectra are combined with ISO SWS and LWS spectra and fluxes from the literature to present a detailed spectrum of the source spanning three orders of magnitude in wavelength. The quality of the spectrum longwards of 100 {mu}m allows us to fit a single temperature greybody with temperature 80.3pm0.6K and dust emissivity index 1.73pm0.02, an accuracy rarely obtained with previous instruments. We estimate a mass of 1500 Msol for the clump containing the HII region. The clumps bolometeric luminosity of 4 x 10^6 Lsol is comparable to, or slightly greater than, the known O-star powering the UCHII region.
We describe our custom processing of the entire Wide-field Infrared Survey Explorer (WISE) 12 micron imaging data set, and present a high-resolution, full-sky map of diffuse Galactic dust emission that is free of compact sources and other contaminating artifacts. The principal distinctions between our resulting co-added images and the WISE Atlas stacks are our removal of compact sources, including their associated electronic and optical artifacts, and our preservation of spatial modes larger than 1.5 degrees. We provide access to the resulting full-sky map via a set of 430 12.5 degree by 12.5 degree mosaics. These stacks have been smoothed to 15 resolution and are accompanied by corresponding coverage maps, artifact images, and bit-masks for point sources, resolved compact sources, and other defects. When combined appropriately with other mid-infrared and far-infrared data sets, we expect our WISE 12 micron co-adds to form the basis for a full-sky dust extinction map with angular resolution several times better than Schlegel et al. (1998).
The vast quantity of strong galaxy-galaxy gravitational lenses expected by future large-scale surveys necessitates the development of automated methods to efficiently model their mass profiles. For this purpose, we train an approximate Bayesian convolutional neural network (CNN) to predict mass profile parameters and associated uncertainties, and compare its accuracy to that of conventional parametric modelling for a range of increasingly complex lensing systems. These include standard smooth parametric density profiles, hydrodynamical EAGLE galaxies and the inclusion of foreground mass structures, combined with parametric sources and sources extracted from the Hubble Ultra Deep Field. In addition, we also present a method for combining the CNN with traditional parametric density profile fitting in an automated fashion, where the CNN provides initial priors on the latters parameters. On average, the CNN achieved errors 19 $pm$ 22 per cent lower than the traditional methods blind modelling. The combination method instead achieved 27 $pm$ 11 per cent lower errors over the blind modelling, reduced further to 37 $pm$ 11 per cent when the priors also incorporated the CNN-predicted uncertainties, with errors also 17 $pm$ 21 per cent lower than the CNN by itself. While the CNN is undoubtedly the fastest modelling method, the combination of the two increases the speed of conventional fitting alone by factors of 1.73 and 1.19 with and without CNN-predicted uncertainties, respectively. This, combined with greatly improved accuracy, highlights the benefits one can obtain through combining neural networks with conventional techniques in order to achieve an efficient automated modelling approach.
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