Search

The origin of the radiation observed in the region of the supernova remnant RX J1713.7−3946, one of the brightest TeV emitters, has been debated since its discovery. The existence of atomic and molecular clouds in this object supports the idea that part of the GeV gamma-ray emission in this region originates from proton–proton collisions. However, the observed column density of protons derived from observations of the gas cannot explain the whole emission. Yet there could be a fraction of protons contained in fainter structures that have not been detected so far. Here we search for faint objects in the line of sight of RX J1713.7−3946 using the principle of light extinction and the European Space Agency’s Gaia mission Data Release 2 astrometric and photometric data. We reveal and locate with precision a number of dust clouds and note that only one appears to be in the vicinity of RX J1713.7−3946. We estimate the embedded mass to be Mdust = (7.0 ± 0.6) × 103 M⊙, which might be big enough to contain the missing protons. Finally, using the fact that the supernova remnant is expected to be located in a dusty environment and that there appears to be only one such structure in the vicinity of RX J1713.7−3946, we set a very precise constraint on the supernova remnant distance, at 1.12 ± 0.01 kpc. A dust cloud in the line of sight towards supernova remnant RX J1713.7−3946—identified using Gaia data —contributes to the GeV gamma-ray emission of the region.

We further elaborate on the generalized formulation for cubic equation of state proposed by Cismondi and Mollerup [Fluid Phase Equilib. 232 (2005)]. With this formulation all well-known cubic equations of state can be described with a certain pair of values, which allows for a generic implementation of different equations of state. Based on this generalized formulation, we derive a complete thermodynamic model for computational fluid dynamics (CFD) simulations by providing the resulting correlations for all required thermodynamic properties. For the transport properties, we employ the Chung correlations. Our generic implementation includes the often used equations of state Soave-Redlich-Kwong and Peng-Robinson and the Redlich-Kwong-Peng-Robinson (RKPR) equation of state. The first two assume a universal compressibility factor and are therefore only suitable for fluids with a matching critical compressibility. The Redlich-Kwong-Peng-Robinson overcomes this limitation by considering the equation of state parameter as function of the critical compressibility. We compare the resulting thermodynamic modeling for the three equations of state for selected fluids with each other and CoolProp reference data. As supplementary material to this paper, we provide a Python tool called real gas thermodynamic python library (realtpl). This tool can be used to evaluate and compare the results for a wide range of different fluids. Additionally, we also provide the implementation of the generalized form in OpenFOAM.

Aims. Mapping the interstellar medium in 3D provides a wealth of insights into its inner working. The Milky Way is the only galaxy for which detailed 3D mapping can be achieved in principle. In this paper, we reconstruct the dust density in and around the local super-bubble. Methods. The combined data from surveys such as Gaia, 2MASS, PANSTARRS, and ALLWISE provide the necessary information to make detailed maps of the interstellar medium in our surrounding. To this end, we used variational inference and Gaussian processes to model the dust extinction density, exploiting its intrinsic correlations. Results. We reconstructed a highly resolved dust map, showing the nearest dust clouds at a distance of up to 400 pc with a resolution of 1 pc. Conclusions. Our reconstruction provides insights into the structure of the interstellar medium. We compute summary statistics of the spectral index and the 1-point function of the logarithmic dust extinction density, which may constrain simulations of the interstellar medium that achieve a similar resolution.

The neocortex plays a key role in cognition, volitional motor control and sensory perception and has undergone tremendous expansion during evolution. The mature neocortex consists of radially aligned neurons that are arranged in six layers. Layers II-VI are often split into two groups: deep and upper layers, both building up the so-called cortical plate during embryonic and foetal development. So far cortical neurogenesis, including the generation of deep and upper layers, has mostly been studied in laboratory rodents and primates. However, precise data for most companion animals are lacking. This study determined the main period of neurogenesis, specifically the timing of deep and upper layer generation, in the developing domestic cat, pig and sheep neocortex using immunohistochemistry for specific neuronal markers, that is Tbr1 and Brn2. We found that the general sequence of neural events is preserved among cat, pig, sheep and other mammalian species. However, we observed differences in the timing of the overall cortical neurogenic period and occurrence of distinct neural events when these three species were compared. Moreover, our data provide further evidence that the cortical neurogenic period and gestation length might be tightly related. Together, these data expand our current understanding of neocortex development and are important for future studies investigating neocortex development and expansion especially in companion animals.