Noncommutative generalization of SU(n)-principal fiber bundles: a review

This is an extended version of a communication made at the international conference ``Noncommutative Geometry and Physics'' held at Orsay in april 2007. In this proceeding, we make a review of some noncommutative constructions connected to the ordinary fiber bundle theory. The noncommutative algebra is the endomorphism algebra of a SU(n)-vector bundle, and its differential calculus is based on its Lie algebra of derivations. It is shown that this noncommutative geometry contains some of the most important constructions introduced and used in the theory of connections on vector bundles, in particular, what is needed to introduce gauge models in physics, and it also contains naturally the essential aspects of the Higgs fields and its associated mechanics of mass generation. It permits one also to extend some previous constructions, as for instance symmetric reduction of (here noncommutative) connections. From a mathematical point of view, these geometrico-algebraic considerations highlight some new point on view, in particular we introduce a new construction of the Chern characteristic classes

Anyonic Excitations in Fast Rotating Bose Gases Revisited

The role of anyonic excitations in fast rotating harmonically trapped Bose gases in a fractional Quantum Hall state is examined. Standard Chern-Simons anyons as well as "non standard" anyons obtained from a statistical interaction having Maxwell-Chern-Simons dynamics and suitable non minimal coupling to matter are considered. Their respective ability to stabilize attractive Bose gases under fast rotation in the thermodynamical limit is studied. Stability can be obtained for standard anyons while for non standard anyons, stability requires that the range of the corresponding statistical interaction does not exceed the typical wavelenght of the atoms.Comment: 5 pages. Improved version to be published in Phys. Rev. A, including a physical discussion on relevant interactions and scattering regime together with implication on the nature of statistical interactio

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

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]

Neutral carbon in the Egg Nebula (AFGL 2688)

A search for sub-mm C I emission from seven stars that are surrounded by dense molecular gas shells led to the detection, in the case of the "Egg Nebula' (AFGL 2688), of an 0.9 K line implying a C I/CO value greater than 5. The material surrounding this star must be extremely carbon-rich, and it is suggested that the apparently greater extent of the C I emission region may be due to the effects of the galactic UV field on the shell's chemistry, as suggested by Huggins and Glassgold (1982)

Machine Learning in XENON1T Analysis

In process of analyzing large amounts of quantitative data, it can be quite time consuming and challenging to uncover populations of interest contained amongst the background data. Therefore, the ability to partially automate the process while gaining additional insight into the interdependencies of key parameters via machine learning seems quite appealing. As of now, the primary means of reviewing the data is by manually plotting data in different parameter spaces to recognize key features, which is slow and error prone. In this experiment, many well-known machine learning algorithms were applied to a dataset to attempt to semi-automatically identify known populations, and potentially identify other features of interest such as detector artefacts. Additionally, using the results of the machine learning process it became possible to cross-check the results of the XENON1T selection cuts. Clustering algorithms were used to segment the dataset into populations, which then recursively split those into additional subpopulations. Upon capturing a subpopulation, a classifier was trained and used to predict if other data could potentially belong to the same population. From this process, it was observed that there were two clustering algorithms that were capable of identifying the electronic recoil band accurately. It was also seen that a few XENON1T selection cuts may need relaxed. These algorithms may be able to be used to tweak the cuts, or continue in search of artefacts. The process of automating the analysis stage by means of machine learning could be further extended by automating the recognition of waveforms using neural networks