An original interferometric study of NGC 1068 with VISIR BURST mode images

We present 12.8 microns images of the core of NGC 1068 obtained with the BURST mode of the VLT/VISIR. We trace structures under the diffraction limit of one UT and we investigate the link between dust in the vicinity of the central engine of NGC 1068, recently resolved by interferometry with MIDI, and more extended structures. This step is mandatory for a multi-scale understanding of the sources of mid-infrared emission in AGNs. A speckle processing of VISIR BURST mode images was performed to extract very low spatial-frequency visibilities, first considering the full field of VISIR BURST mode images and then limiting it to the mask used for the acquisition of MIDI data. Extracted visibilities are reproduced with a multi-component model. We identify two major sources of emission: one compact < 85 mas, associated with the dusty torus, and an elliptical one, (< 140) mas x 1187 mas at P.A.=-4 degrees from N to E. This is consistent with previous deconvolution processes. The combination with MIDI data reveals the close environment of the dusty torus to contribute to about 83 percent of the MIR flux seen by MIDI. This strong contribution has to be considered in modeling long baseline interferometric data. It must be related to the NS elongated component which is thought to originate from individually unresolved dusty clouds and is located inside the ionization cone. Low temperatures of the dusty torus are not challenged, emphasizing the scenarios of clumpy torus.Comment: 10 pages, 7 figures, accepted for publication in A&

A population Monte Carlo scheme with transformed weights and its application to stochastic kinetic models

This paper addresses the problem of Monte Carlo approximation of posterior probability distributions. In particular, we have considered a recently proposed technique known as population Monte Carlo (PMC), which is based on an iterative importance sampling approach. An important drawback of this methodology is the degeneracy of the importance weights when the dimension of either the observations or the variables of interest is high. To alleviate this difficulty, we propose a novel method that performs a nonlinear transformation on the importance weights. This operation reduces the weight variation, hence it avoids their degeneracy and increases the efficiency of the importance sampling scheme, specially when drawing from a proposal functions which are poorly adapted to the true posterior. For the sake of illustration, we have applied the proposed algorithm to the estimation of the parameters of a Gaussian mixture model. This is a very simple problem that enables us to clearly show and discuss the main features of the proposed technique. As a practical application, we have also considered the popular (and challenging) problem of estimating the rate parameters of stochastic kinetic models (SKM). SKMs are highly multivariate systems that model molecular interactions in biological and chemical problems. We introduce a particularization of the proposed algorithm to SKMs and present numerical results.Comment: 35 pages, 8 figure

Assessment of fine scale population genetic diversity and regeneration in Congo basin logged forests

In the Congo Basin most of the light-demanding timber tree species display a deficit of natural regeneration which is a major handicap for sustainable production and certification. Whilst the majority of scientists investigate abiotic and biotic factors explaining that pattern, we hypothesize that tree population density or individual spatial isolation may also affect the tree fitness through inbreeding. In this study, we integrate ecological and genetic approaches to characterize the regeneration potential of a set of priority timber species by (i) estimating pollen dispersal distances at various tree population densities, and (ii) evaluating the impact of increasing spatial isolation on mating characteristics and tree fitness. The ultimate goal is the proposal of minimum population density that prevents inbreeding consequences. Method This ongoing study focuses on 10 timber species (Pericopsis elata, Milicia excelsa, Baillonella toxisperma, Entandrophragma cylindricum, E. utile, E. angolense, E. candollei, Afzelia bipindensis, Erythrophleum suaveloens, Terminalia superba). The data collection was carried out in the logging concession granted to Pallisco in Cameroon. We established two 400-ha plots, where all individuals (DBH > 10 cm) of the target species were inventoried and mapped. A sample of leave or cambium was collected for each of these individuals, as well as for seedlings to characterize patterns of gene flow using genetic tools (nuclear microsatellites). Dispersal agents were identified by direct observations and camera traps. Germination success was characterized in nursery for seeds collected on trees under an increasing isolation gradient. Results Main dispersal agents (wind, bat, rodent) and predators (rodent) were identified for all the species. The gene flow and germination data is still being analyzed and the main results will be presented in the poster. Conclusion Our data will allow characterizing the reproductive biology of a set of important timber species from the Congo basin. These information will strengthen sustainable forest management and the application of certification by adjusting harvesting norms through the use of scientifically-relevant data. In particular, we will tentatively define a maximum distance to be maintained between two adults to allow a qualitative reproduction

Adaptive Importance Sampling in General Mixture Classes

In this paper, we propose an adaptive algorithm that iteratively updates both the weights and component parameters of a mixture importance sampling density so as to optimise the importance sampling performances, as measured by an entropy criterion. The method is shown to be applicable to a wide class of importance sampling densities, which includes in particular mixtures of multivariate Student t distributions. The performances of the proposed scheme are studied on both artificial and real examples, highlighting in particular the benefit of a novel Rao-Blackwellisation device which can be easily incorporated in the updating scheme.Comment: Removed misleading comment in Section

What can we learn about protoplanetary disks from analysis of mid-infrared carbonaceous dust emission?

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 "Gomez's Hamburger" which are poorly characterized.Comment: Accepted for publication in A&

Kernel Sequential Monte Carlo

We propose kernel sequential Monte Carlo (KSMC), a framework for sampling from static target densities. KSMC is a family of sequential Monte Carlo algorithms that are based on building emulator models of the current particle system in a reproducing kernel Hilbert space. We here focus on modelling nonlinear covariance structure and gradients of the target. The emulator’s geometry is adaptively updated and subsequently used to inform local proposals. Unlike in adaptive Markov chain Monte Carlo, continuous adaptation does not compromise convergence of the sampler. KSMC combines the strengths of sequental Monte Carlo and kernel methods: superior performance for multimodal targets and the ability to estimate model evidence as compared to Markov chain Monte Carlo, and the emulator’s ability to represent targets that exhibit high degrees of nonlinearity. As KSMC does not require access to target gradients, it is particularly applicable on targets whose gradients are unknown or prohibitively expensive. We describe necessary tuning details and demonstrate the benefits of the the proposed methodology on a series of challenging synthetic and real-world examples