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In this Paper we analyze the mid-infrared (mid-IR) emission of very small dust particles in a sample of 12 protoplanetary disks to see how they are connected to interstellar dust particles and to investigate the possibility that their emission can be used as a probe of the physical conditions and evolution of the disk. We define a basis made of three mid-IR template spectra PAH$^0$, PAH$^+$ and VSGs that were derived from the analysis of reflection nebulae, and an additional PAH$^x$ spectrum that was introduced by Joblin et al. (2008) for the analysis of the spectra of planetary nebulae. From the optimization of the fit of 12 star+disk spectra, using a linear combination of the 4 template spectra, we found that an additional small grain component with a broad feature at 8.3 $mu$m is needed. We find that the fraction of VSG emission in disks decreases with increasing stellar temperature. VSGs appear to be destroyed by UV photons at the surface of disks, thus releasing free PAH molecules, which are eventually ionized as it is observed in photodissociation regions. On the opposite, we observe that the fraction of PAH$^x$ increases with increasing star temperature except in the case of B stars where they are absent. We argue that this is compatible with the identification of PAH$^x$ as large ionized PAHs, most likely emitting in regions of the disk that are close to the star. Finally, we provide a UV-dependant scheme to explain the evolution of PAHs and VSGs in protoplanetary disks. We show that A stars modify the size spectrum of PAHs and VSGs in favor of large PAHs while B stars destroy even the largest PAHs up to large radii in the disk. These results allow us to put new constrains on the properties of two sources: IRS 48 and Gomezs Hamburger which are poorly characterized.
Ultra-relativistic electromagnetic plasmas can be used for improving our understanding of the quark-gluon plasma. In the weakly coupled regime both plasmas can be described by transport theoretical and quantum field theoretical methods leading to sim
We investigate the effects of multi-task learning using the recently introduced task of semantic tagging. We employ semantic tagging as an auxiliary task for three different NLP tasks: part-of-speech tagging, Universal Dependency parsing, and Natural
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