Disc formation in turbulent cloud cores: Circumventing the magnetic braking catastrophe

We present collapse simulations of strongly magnetised, 100 M_sun, turbulent cloud cores. Around the protostars formed during the collapse Keplerian discs with typical sizes of up to 100 AU build up in contrast to previous simulations neglecting turbulence. Analysing the condensations in which the discs form, we show that the magnetic flux loss is not sufficient to explain the build-up of Keplerian discs. The average magnetic field is strongly inclined to the disc which might reduce the magnetic braking efficiency. However, the main reason for the reduced magnetic braking efficiency is the highly disordered magnetic field in the surroundings of the discs. Furthermore, due to the lack of a coherently rotating structure in the turbulent environment of the disc no toroidal magnetic field necessary for angular momentum extraction can build up. Simultaneously the angular momentum inflow remains high due to local shear flows created by the turbulent motions. We suggest that the "magnetic braking catastrophe" is an artefact of the idealised non-turbulent initial conditions and that turbulence provides a natural mechanism to circumvent this problem.Comment: 4 pages, 2 figures. To appear in the proceedings of 'The Labyrinth of Star Formation' (18-22 June 2012, Chania, Greece), published by Springe

Decoherence and entropy of primordial fluctuations II. The entropy budget

We calculate the entropy of adiabatic perturbations associated with a truncation of the hierarchy of Green functions at the first non trivial level, i.e. in a self-consistent Gaussian approximation. We give the equation governing the entropy growth and discuss its phenomenology. It is parameterized by two model-dependent kernels. We then examine two particular inflationary models, one with isocurvature perturbations, the other with corrections due to loops of matter fields. In the first model the entropy grows rapidely, while in the second the state remains pure (at one loop).Comment: 28 page

Creation of Entanglement by Interaction with a Common Heat Bath

I show that entanglement between two qubits can be generated if the two qubits interact with a common heat bath in thermal equilibrium, but do not interact directly with each other. In most situations the entanglement is created for a very short time after the interaction with the heat bath is switched on, but depending on system, coupling, and heat bath, the entanglement may persist for arbitrarily long times. This mechanism sheds new light on the creation of entanglement. A particular example of two quantum dots in a closed cavity is discussed, where the heat bath is given by the blackbody radiation.Comment: 4 revtex pages, 1 eps figure; replaced with published version; short discussion on entanglement distillation adde

The General Correlation Function in the Schwinger Model on a Torus

In the framework of the Euclidean path integral approach we derive the exact formula for the general N-point chiral densities correlator in the Schwinger model on a torusComment: 17 pages, misprints corrected, references adde

A large-scale evaluation framework for EEG deep learning architectures

EEG is the most common signal source for noninvasive BCI applications. For such applications, the EEG signal needs to be decoded and translated into appropriate actions. A recently emerging EEG decoding approach is deep learning with Convolutional or Recurrent Neural Networks (CNNs, RNNs) with many different architectures already published. Here we present a novel framework for the large-scale evaluation of different deep-learning architectures on different EEG datasets. This framework comprises (i) a collection of EEG datasets currently including 100 examples (recording sessions) from six different classification problems, (ii) a collection of different EEG decoding algorithms, and (iii) a wrapper linking the decoders to the data as well as handling structured documentation of all settings and (hyper-) parameters and statistics, designed to ensure transparency and reproducibility. As an applications example we used our framework by comparing three publicly available CNN architectures: the Braindecode Deep4 ConvNet, Braindecode Shallow ConvNet, and two versions of EEGNet. We also show how our framework can be used to study similarities and differences in the performance of different decoding methods across tasks. We argue that the deep learning EEG framework as described here could help to tap the full potential of deep learning for BCI applications.Comment: 7 pages, 3 figures, final version accepted for presentation at IEEE SMC 2018 conferenc

Decoherence: Concepts and Examples

We give a pedagogical introduction to the process of decoherence - the irreversible emergence of classical properties through interaction with the environment. After discussing the general concepts, we present the following examples: Localisation of objects, quantum Zeno effect, classicality of fields and charges in QED, and decoherence in gravity theory. We finally emphasise the important interpretational features of decoherence.Comment: 24 pages, LATEX, 9 figures, needs macro lamuphys.sty, to appear in the Proceedings of the 10th Born Symposiu

Change of decoherence scenario and appearance of localization due to reservoir anharmonicity

Although coupling to a super-Ohmic bosonic reservoir leads only to partial dephasing on short time scales, exponential decay of coherence appears in the Markovian limit (for long times) if anharmonicity of the reservoir is taken into account. This effect not only qualitatively changes the decoherence scenario but also leads to localization processes in which superpositions of spatially separated states dephase with a rate that depends on the distance between the localized states. As an example of the latter process, we study the decay of coherence of an electron state delocalized over two semiconductor quantum dots due to anharmonicity of phonon modes.Comment: 4 pages, 1 figure; moderate changes; auxiliary material added; to appear in Phys. Rev. Let