Non-thermal radio emission from O-type stars. V. 9 Sgr

The colliding winds in a massive binary system generate synchrotron emission due to a fraction of electrons that have been accelerated to relativistic speeds around the shocks in the colliding-wind region. We studied the radio light curve of 9 Sgr = HD 164794, a massive O-type binary with a 9.1-yr period. We investigated whether the radio emission varies consistently with orbital phase and we determined some parameters of the colliding-wind region. We reduced a large set of archive data from the Very Large Array (VLA) to determine the radio light curve of 9 Sgr at 2, 3.6, 6 and 20 cm. We also constructed a simple model that solves the radiative transfer in the colliding-wind region and both stellar winds. The 2-cm radio flux shows clear phase-locked variability with the orbit. The behaviour at other wavelengths is less clear, mainly due to a lack of observations centred on 9 Sgr around periastron passage. The high fluxes and nearly flat spectral shape of the radio emission show that synchrotron radiation dominates the radio light curve at all orbital phases. The model provides a good fit to the 2-cm observations, allowing us to estimate that the brightness temperature of the synchrotron radiation emitted in the colliding-wind region at 2 cm is at least 4 x 10^8 K. The simple model used here already allows us to derive important information about the colliding-wind region. We propose that 9 Sgr is a good candidate for more detailed modelling, as the colliding-wind region remains adiabatic during the whole orbit thus simplifying the hydrodynamics.Comment: 10 pages, 3 figures, accepted for publication in A&

Addressing Model Vulnerability to Distributional Shifts over Image Transformation Sets

We are concerned with the vulnerability of computer vision models to distributional shifts. We formulate a combinatorial optimization problem that allows evaluating the regions in the image space where a given model is more vulnerable, in terms of image transformations applied to the input, and face it with standard search algorithms. We further embed this idea in a training procedure, where we define new data augmentation rules according to the image transformations that the current model is most vulnerable to, over iterations. An empirical evaluation on classification and semantic segmentation problems suggests that the devised algorithm allows to train models that are more robust against content-preserving image manipulations and, in general, against distributional shifts.Comment: ICCV 2019 (camera ready

Climate Mitigation, Deforestation and Human Development in Brazil

human development, climate change

Dense semantic labeling of sub-decimeter resolution images with convolutional neural networks

Semantic labeling (or pixel-level land-cover classification) in ultra-high resolution imagery (< 10cm) requires statistical models able to learn high level concepts from spatial data, with large appearance variations. Convolutional Neural Networks (CNNs) achieve this goal by learning discriminatively a hierarchy of representations of increasing abstraction. In this paper we present a CNN-based system relying on an downsample-then-upsample architecture. Specifically, it first learns a rough spatial map of high-level representations by means of convolutions and then learns to upsample them back to the original resolution by deconvolutions. By doing so, the CNN learns to densely label every pixel at the original resolution of the image. This results in many advantages, including i) state-of-the-art numerical accuracy, ii) improved geometric accuracy of predictions and iii) high efficiency at inference time. We test the proposed system on the Vaihingen and Potsdam sub-decimeter resolution datasets, involving semantic labeling of aerial images of 9cm and 5cm resolution, respectively. These datasets are composed by many large and fully annotated tiles allowing an unbiased evaluation of models making use of spatial information. We do so by comparing two standard CNN architectures to the proposed one: standard patch classification, prediction of local label patches by employing only convolutions and full patch labeling by employing deconvolutions. All the systems compare favorably or outperform a state-of-the-art baseline relying on superpixels and powerful appearance descriptors. The proposed full patch labeling CNN outperforms these models by a large margin, also showing a very appealing inference time.Comment: Accepted in IEEE Transactions on Geoscience and Remote Sensing, 201

A reverse KAM method to estimate unknown mutual inclinations in exoplanetary systems

The inclinations of exoplanets detected via radial velocity method are essentially unknown. We aim to provide estimations of the ranges of mutual inclinations that are compatible with the long-term stability of the system. Focusing on the skeleton of an extrasolar system, i.e., considering only the two most massive planets, we study the Hamiltonian of the three-body problem after the reduction of the angular momentum. Such a Hamiltonian is expanded both in Poincar\'e canonical variables and in the small parameter $D_2$, which represents the normalised Angular Momentum Deficit. The value of the mutual inclination is deduced from $D_2$ and, thanks to the use of interval arithmetic, we are able to consider open sets of initial conditions instead of single values. Looking at the convergence radius of the Kolmogorov normal form, we develop a reverse KAM approach in order to estimate the ranges of mutual inclinations that are compatible with the long-term stability in a KAM sense. Our method is successfully applied to the extrasolar systems HD 141399, HD 143761 and HD 40307.Comment: 19 pages, 3 figure

On the 3D secular dynamics of radial-velocity-detected planetary systems

Aims. To date, more than 600 multi-planetary systems have been discovered. Due to the limitations of the detection methods, our knowledge of the systems is usually far from complete. In particular, for planetary systems discovered with the radial velocity (RV) technique, the inclinations of the orbital planes, and thus the mutual inclinations and planetary masses, are unknown. Our work aims to constrain the spatial configuration of several RV-detected extrasolar systems that are not in a mean-motion resonance. Methods. Through an analytical study based on a first-order secular Hamiltonian expansion and numerical explorations performed with a chaos detector, we identified ranges of values for the orbital inclinations and the mutual inclinations, which ensure the long-term stability of the system. Our results were validated by comparison with n-body simulations, showing the accuracy of our analytical approach up to high mutual inclinations (approx. 70{\deg}-80{\deg}). Results. We find that, given the current estimations for the parameters of the selected systems, long-term regular evolution of the spatial configurations is observed, for all the systems, i) at low mutual inclinations (typically less than 35{\deg}) and ii) at higher mutual inclinations, preferentially if the system is in a Lidov-Kozai resonance. Indeed, a rapid destabilisation of highly mutually inclined orbits is commonly observed, due to the significant chaos that develops around the stability islands of the Lidov-Kozai resonance. The extent of the Lidov-Kozai resonant region is discussed for ten planetary systems (HD 11506, HD 12661, HD 134987, HD 142, HD 154857, HD 164922, HD 169830, HD 207832, HD 4732, and HD 74156).Comment: Accepted for publication in A&