The Connection Between Diffuse Light and Intracluster Planetary Nebulae in the Virgo Cluster

We compare the distribution of diffuse intracluster light detected in the Virgo Cluster via broadband imaging with that inferred from searches for intracluster planetary nebulae (IPNe). We find a rough correspondence on large scales (~ 100 kpc) between the two, but with very large scatter (~ 1.3 mag/arcsec^2). On smaller scales (1 -- 10 kpc), the presence or absence of correlation is clearly dependent on the underlying surface brightness. On these scales, we find a correlation in regions of higher surface brightness (mu_V < ~27) which are dominated by the halos of large galaxies such as M87, M86, and M84. In those cases, we are likely tracing PNe associated with galaxies rather than true IPNe. In true intracluster fields, at lower surface brightness, the correlation between luminosity and IPN candidates is much weaker. While a correlation between broadband light and IPNe is expected based on stellar populations, a variety of statistical, physical, and methodological effects can act to wash out this correlation and explain the lack of a strong correlation at lower surface brightness found here. [abridged

Wigner crystallization in a polarizable medium

We present a variational study of the 2D and 3D Wigner crystal phase of large polarons. The method generalizes that introduced by S. Fratini,P.\ Qu{\'{e}}merais [Mod. Phys. Lett. B {\bf 12} 1003 (1998)]. We take into account the Wigner crystal normal modes rather than a single mean frequency in the minimization procedure of the variational free energy. We calculate the renormalized modes of the crystal as well as the charge polarization correlation function and polaron radius. The solid phase boundaries are determined via a Lindemann criterion, suitably generalized to take into account the classical-to-quantum cross-over. In the weak electron-phonon coupling limit, the Wigner crystal parameters are renormalized by the electron-phonon interaction leading to a stabilization of the solid phase for low polarizability of the medium. Conversely, at intermediate and strong coupling, the behavior of the system depends strongly on the polarizability of the medium. For weakly polarizable media, a density crossover occurs inside the solid phase when the renormalized plasma frequency approaches the phonon frequency. At low density, we have a renormalized polaron Wigner crystal, while at higher densities the electron-phonon interaction is weakened irrespective of the {\it bare} electron-phonon coupling. For strongly polarizable media, the system behaves as a Lorentz lattice of dipoles. The abrupt softening of the internal polaronic frequency predicted by Fratini and Quemerais is observed near the actual melting point only at very strong coupling, leading to a possible liquid polaronic phase for a wider range of parameters.Comment: 24 pages, 13 figures v1.

Quicksilver: Fast Predictive Image Registration - a Deep Learning Approach

This paper introduces Quicksilver, a fast deformable image registration method. Quicksilver registration for image-pairs works by patch-wise prediction of a deformation model based directly on image appearance. A deep encoder-decoder network is used as the prediction model. While the prediction strategy is general, we focus on predictions for the Large Deformation Diffeomorphic Metric Mapping (LDDMM) model. Specifically, we predict the momentum-parameterization of LDDMM, which facilitates a patch-wise prediction strategy while maintaining the theoretical properties of LDDMM, such as guaranteed diffeomorphic mappings for sufficiently strong regularization. We also provide a probabilistic version of our prediction network which can be sampled during the testing time to calculate uncertainties in the predicted deformations. Finally, we introduce a new correction network which greatly increases the prediction accuracy of an already existing prediction network. We show experimental results for uni-modal atlas-to-image as well as uni- / multi- modal image-to-image registrations. These experiments demonstrate that our method accurately predicts registrations obtained by numerical optimization, is very fast, achieves state-of-the-art registration results on four standard validation datasets, and can jointly learn an image similarity measure. Quicksilver is freely available as an open-source software.Comment: Add new discussion

Wind Energy and the Turbulent Nature of the Atmospheric Boundary Layer

Wind turbines operate in the atmospheric boundary layer, where they are exposed to the turbulent atmospheric flows. As the response time of wind turbine is typically in the range of seconds, they are affected by the small scale intermittent properties of the turbulent wind. Consequently, basic features which are known for small-scale homogeneous isotropic turbulence, and in particular the well-known intermittency problem, have an important impact on the wind energy conversion process. We report on basic research results concerning the small-scale intermittent properties of atmospheric flows and their impact on the wind energy conversion process. The analysis of wind data shows strongly intermittent statistics of wind fluctuations. To achieve numerical modeling a data-driven superposition model is proposed. For the experimental reproduction and adjustment of intermittent flows a so-called active grid setup is presented. Its ability is shown to generate reproducible properties of atmospheric flows on the smaller scales of the laboratory conditions of a wind tunnel. As an application example the response dynamics of different anemometer types are tested. To achieve a proper understanding of the impact of intermittent turbulent inflow properties on wind turbines we present methods of numerical and stochastic modeling, and compare the results to measurement data. As a summarizing result we find that atmospheric turbulence imposes its intermittent features on the complete wind energy conversion process. Intermittent turbulence features are not only present in atmospheric wind, but are also dominant in the loads on the turbine, i.e. rotor torque and thrust, and in the electrical power output signal. We conclude that profound knowledge of turbulent statistics and the application of suitable numerical as well as experimental methods are necessary to grasp these unique features (...)Comment: Accepted by the Journal of Turbulence on May 17, 201

Blind source separation using temporal predictability

A measure of temporal predictability is defined and used to separate linear mixtures of signals. Given any set of statistically independent source signals, it is conjectured here that a linear mixture of those signals has the following property: the temporal predictability of any signal mixture is less than (or equal to) that of any of its component source signals. It is shown that this property can be used to recover source signals from a set of linear mixtures of those signals by finding an un-mixing matrix that maximizes a measure of temporal predictability for each recovered signal. This matrix is obtained as the solution to a generalized eigenvalue problem; such problems have scaling characteristics of O (N3), where N is the number of signal mixtures. In contrast to independent component analysis, the temporal predictability method requires minimal assumptions regarding the probability density functions of source signals. It is demonstrated that the method can separate signal mixtures in which each mixture is a linear combination of source signals with supergaussian, sub-gaussian, and gaussian probability density functions and on mixtures of voices and music