Accuracy of magnetic energy computations

For magnetically driven events, the magnetic energy of the system is the prime energy reservoir that fuels the dynamical evolution. In the solar context, the free energy is one of the main indicators used in space weather forecasts to predict the eruptivity of active regions. A trustworthy estimation of the magnetic energy is therefore needed in three-dimensional models of the solar atmosphere, eg in coronal fields reconstructions or numerical simulations. The expression of the energy of a system as the sum of its potential energy and its free energy (Thomson's theorem) is strictly valid when the magnetic field is exactly solenoidal. For numerical realizations on a discrete grid, this property may be only approximately fulfilled. We show that the imperfect solenoidality induces terms in the energy that can lead to misinterpreting the amount of free energy present in a magnetic configuration. We consider a decomposition of the energy in solenoidal and nonsolenoidal parts which allows the unambiguous estimation of the nonsolenoidal contribution to the energy. We apply this decomposition to six typical cases broadly used in solar physics. We quantify to what extent the Thomson theorem is not satisfied when approximately solenoidal fields are used. The quantified errors on energy vary from negligible to significant errors, depending on the extent of the nonsolenoidal component. We identify the main source of errors and analyze the implications of adding a variable amount of divergence to various solenoidal fields. Finally, we present pathological unphysical situations where the estimated free energy would appear to be negative, as found in some previous works, and we identify the source of this error to be the presence of a finite divergence. We provide a method of quantifying the effect of a finite divergence in numerical fields, together with detailed diagnostics of its sources

Orientation and distribution of recent gullies in the southern hemisphere of Mars: observations from HRSC/MEX and MOC/MGS data

Abstract not available

The relativistic solar particle event of 2005 January 20: origin of delayed particle acceleration

The highest energies of solar energetic nucleons detected in space or through gamma-ray emission in the solar atmosphere are in the GeV range. Where and how the particles are accelerated is still controversial. We search for observational information on the location and nature of the acceleration region(s) by comparing the timing of relativistic protons detected on Earth and radiative signatures in the solar atmosphere during the particularly well-observed 2005 Jan. 20 event. This investigation focuses on the post-impulsive flare phase, where a second peak was observed in the relativistic proton time profile by neutron monitors. This time profile is compared in detail with UV imaging and radio spectrography over a broad frequency band from the low corona to interplanetary space. It is shown that the late relativistic proton release to interplanetary space was accompanied by a distinct new episode of energy release and electron acceleration in the corona traced by the radio emission and by brightenings of UV kernels. These signatures are interpreted in terms of magnetic restructuring in the corona after the coronal mass ejection passage. We attribute the delayed relativistic proton acceleration to magnetic reconnection and possibly to turbulence in large-scale coronal loops. While Type II radio emission was observed in the high corona, no evidence of a temporal relationship with the relativistic proton acceleration was found

Belief Hierarchical Clustering

In the data mining field many clustering methods have been proposed, yet standard versions do not take into account uncertain databases. This paper deals with a new approach to cluster uncertain data by using a hierarchical clustering defined within the belief function framework. The main objective of the belief hierarchical clustering is to allow an object to belong to one or several clusters. To each belonging, a degree of belief is associated, and clusters are combined based on the pignistic properties. Experiments with real uncertain data show that our proposed method can be considered as a propitious tool

Mass predictions, partial difference equations and higher‐order isospin effects

The Garvey‐Kelson mass relation has been extended by introducing inhomogeneous source terms to improve problems with long‐range extrapolations. Such mass relations are third‐order partial difference equations with solutions representing mass equations. It was found that inhomogeneous source terms based on shell‐dependent Coulomb and symmetry energy terms are not sufficient to improve upon extrapolations. However, contributions from higher‐order perturbations in isospin (mostly cubic) have a significant effect. A many‐parameter mass equation was constructed as the solution of an inhomogeneous difference equation with properly adjusted shell‐dependent source terms. The standard deviation for reproducing the experimental mass values is σm=194 keV. Nuclear contributions were subjected to the constraint of charge symmetry, and Coulomb displacement energies are reproduced with σc=41 keV. Mass predictions for over 4000 nuclei with A≳16 and both N≥Z and N<Z (except N=Z=odd for A<40) are reported.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/87305/2/62_1.pd

A non parametric linear feature extraction approach to texture classification

A non parametric approach to linear feature extraction is presented . The theoretical background is introduced with a new derivation of the equation that gives the best scalar extractor according to the Patrick-Fischer distance [17] . The main characteristics of the implementation are given. The application of the method to the classification of some binary synthetic textures with a natural visual aspect [15] leads to results better than those based on the Fisher discriminant analysis [7] .On présente une approche non paramétrique de l'extraction linéaire de caractéristiques et son application à la classification de textures. Le cadre théorique de l'étude est rappelé et on donne une nouvelle présentation de l'équation de l'extracteur optimal de caractéristiques selon la distance de Patrick-Fischer [17] . Les grandes lignes de la mise en oeuvre de cette méthode sont présentées . La classification de textures synthétiques binaires ayant un aspect visuel naturel [15] est ensuite abordée ; sur les exemples étudiés, on constate que la méthode proposée est meilleure, en terme de taux de bonne classification, que le classifieur basé sur l'analyse discriminante de Fisher [7]