95,514 research outputs found
Ks band secondary eclipses of WASP-19b and WASP-43b with the Anglo-Australian Telescope
We report new Ks band secondary eclipse observations for the hot-Jupiters
WASP-19b and WASP-43b. Using the IRIS2 infrared camera on the Anglo-Australian
Telescope (AAT), we measured significant secondary eclipses for both planets,
with depths of 0.287 -0.020/+0.020% and 0.181 -0.027/+0.027% for WASP-19b and
WASP-43b respectively. We compare the observations to atmosphere models from
the VSTAR line-by-line radiative transfer code, and examine the effect of C/O
abundance, top layer haze, and metallicities on the observed spectra. We
performed a series of signal injection and recovery exercises on the observed
light curves to explore the detection thresholds of the AAT+IRIS2 facility. We
find that the optimal photometric precision is achieved for targets brighter
than Kmag = 9, for which eclipses as shallow as 0.05% are detectable at >5
sigma significance.Comment: Accepted for publication in MNRAS, 13 pages, 10 figure
Improved Compressive Sensing Of Natural Scenes Using Localized Random Sampling
Compressive sensing (CS) theory demonstrates that by using uniformly-random sampling, rather than uniformly-spaced sampling, higher quality image reconstructions are often achievable. Considering that the structure of sampling protocols has such a profound impact on the quality of image reconstructions, we formulate a new sampling scheme motivated by physiological receptive field structure, localized random sampling, which yields significantly improved CS image reconstructions. For each set of localized image measurements, our sampling method first randomly selects an image pixel and then measures its nearby pixels with probability depending on their distance from the initially selected pixel. We compare the uniformly-random and localized random sampling methods over a large space of sampling parameters, and show that, for the optimal parameter choices, higher quality image reconstructions can be consistently obtained by using localized random sampling. In addition, we argue that the localized random CS optimal parameter choice is stable with respect to diverse natural images, and scales with the number of samples used for reconstruction. We expect that the localized random sampling protocol helps to explain the evolutionarily advantageous nature of receptive field structure in visual systems and suggests several future research areas in CS theory and its application to brain imaging
Efficient Image Processing Via Compressive Sensing Of Integrate-And-Fire Neuronal Network Dynamics
Integrate-and-fire (I&F) neuronal networks are ubiquitous in diverse image processing applications, including image segmentation and visual perception. While conventional I&F network image processing requires the number of nodes composing the network to be equal to the number of image pixels driving the network, we determine whether I&F dynamics can accurately transmit image information when there are significantly fewer nodes than network input-signal components. Although compressive sensing (CS) theory facilitates the recovery of images using very few samples through linear signal processing, it does not address whether similar signal recovery techniques facilitate reconstructions through measurement of the nonlinear dynamics of an I&F network. In this paper, we present a new framework for recovering sparse inputs of nonlinear neuronal networks via compressive sensing. By recovering both one-dimensional inputs and two-dimensional images, resembling natural stimuli, we demonstrate that input information can be well-preserved through nonlinear I&F network dynamics even when the number of network-output measurements is significantly smaller than the number of input-signal components. This work suggests an important extension of CS theory potentially useful in improving the processing of medical or natural images through I&F network dynamics and understanding the transmission of stimulus information across the visual system
Neutron halo in deformed nuclei from a relativistic Hartree-Bogoliubov model in a Woods-Saxon basis
Halo phenomenon in deformed nuclei is studied by using a fully
self-consistent deformed relativistic Hartree-Bogoliubov model in a spherical
Woods-Saxon basis with the proper asymptotic behavior at large distance from
the nuclear center. Taking a deformed neutron-rich and weakly bound nucleus
Mg as an example and by examining contributions of the halo, deformation
effects, and large spatial extensions, we show a decoupling of the halo
orbitals from the deformation of the core.Comment: 6 pages, 2 figures, to appear in the proceedings of the International
Nuclear Physics Conference (INPC 2010), July 4-9 2010, Vancouve
The upper-atmosphere extension of the ICON general circulation model (version: Ua-icon-1.0)
How the upper-atmosphere branch of the circulation contributes to and interacts with the circulation of the middle and lower atmosphere is a research area with many open questions. Inertia-gravity waves, for instance, have moved in the focus of research as they are suspected to be key features in driving and shaping the circulation. Numerical atmospheric models are an important pillar for this research. We use the ICOsahedral Non-hydrostatic (ICON) general circulation model, which is a joint development of the Max Planck Institute for Meteorology (MPI-M) and the German Weather Service (DWD), and provides, e.g., local mass conservation, a flexible grid nesting option, and a non-hydrostatic dynamical core formulated on an icosahedral-triangular grid. We extended ICON to the upper atmosphere and present here the two main components of this new configuration named UA-ICON: an extension of the dynamical core from shallow- to deep-atmosphere dynamics and the implementation of an upper-atmosphere physics package. A series of idealized test cases and climatological simulations is performed in order to evaluate the upper-atmosphere extension of ICON. © Author(s) 2019
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