Testing dark matter and geometry sustained circular velocities in the Milky Way with Gaia DR2

Flat rotation curves in disk galaxies represent the main evidence for large amounts of surrounding dark matter. Despite of the difficulty in identifying the dark matter contribution to the total mass density in our Galaxy, stellar kinematics, as tracer of gravitational potential, is the most reliable observable for gauging different matter components. This work tests the flatness of the MW rotation curve with a simple general relativistic model suitable to represent the geometry of a disk as a stationary axisymmetric dust metric at a sufficiently large distance from a central body. Circular velocities of unprecedented accuracy were derived from the Gaia DR2 data for a carefully selected sample of disk stars. We then fit these velocities to both the classical, i.e. including a dark matter halo, rotation curve model and a relativistic analogue, as derived form the solution of Einstein's equation. The GR-compliant MW rotational curve model results statistically indistinguishable from its state-of-the-art DM analogue. This supports our ansatz that a stationary and axisymmetric galaxy-scale metric could "fill the gap" in a baryons-only Milky Way, suggestive of star orbits dragged along the background geometry. We confirmed that geometry is a manifestation of gravity according to the Einstein theory, in particular the weak gravitational effect due to the off-diagonal term of the metric could mimic for a "DM-like" effect in the observed flatness of the MW rotation curve. In the context of Local Cosmology, our findings are suggestive of a Galaxy phase-space as the exterior gravitational field of a Kerr-like source (inner rotating bulge) without the need of extra-matter.Comment: Acknowledgments and references updated; 18 pages, 2 figures, improved version after referee's comment

Deferring the learning for better generalization in radial basis neural networks

Proceeding of: International Conference Artificial Neural Networks — ICANN 2001. Vienna, Austria, August 21–25, 2001The level of generalization of neural networks is heavily dependent on the quality of the training data. That is, some of the training patterns can be redundant or irrelevant. It has been shown that with careful dynamic selection of training patterns, better generalization performance may be obtained. Nevertheless, generalization is carried out independently of the novel patterns to be approximated. In this paper, we present a learning method that automatically selects the most appropriate training patterns to the new sample to be predicted. The proposed method has been applied to Radial Basis Neural Networks, whose generalization capability is usually very poor. The learning strategy slows down the response of the network in the generalisation phase. However, this does not introduces a significance limitation in the application of the method because of the fast training of Radial Basis Neural Networks

Gluon Condensate and Beyond

We review briefly and in retrospect the development which brought about the QCD sum rules based on introduction of the gluon condensate (M.A. Shifman, A.I. Vainshtein, and V.I. Zakharov (1978)).Comment: 15 pages, 5 figures, uses sprocl.sty (included). The 1999 Sakurai Prize Lectur

Spin dynamics in electrochemically charged CdSe quantum dots

We use time-resolved Faraday rotation to measure coherent spin dynamics in colloidal CdSe quantum dots charged in an electrochemical cell at room temperature. Filling of the 1Se electron level is demonstrated by the bleaching of the 1Se-1S3/2 absorption peak. One of the two Lande g-factors observed in uncharged quantum dots disappears upon filling of the 1Se electron state. The transverse spin coherence time, which is over 1 ns and is limited by inhomogeneous dephasing, also appears to increase with charging voltage. The amplitude of the spin precession signal peaks near the half-filling potential. Its evolution at charging potentials without any observable bleaching of the 1Se-1S3/2 transition suggests that the spin dynamics are influenced by low-energy surface states.Comment: 4 pages, 4 figure

Local Manipulation of Nuclear Spin in a Semiconductor Quantum Well

The shaping of nuclear spin polarization profiles and the induction of nuclear resonances are demonstrated within a parabolic quantum well using an externally applied gate voltage. Voltage control of the electron and hole wave functions results in nanometer-scale sheets of polarized nuclei positioned along the growth direction of the well. RF voltages across the gates induce resonant spin transitions of selected isotopes. This depolarizing effect depends strongly on the separation of electrons and holes, suggesting that a highly localized mechanism accounts for the observed behavior.Comment: 18 pages, 4 figure

Observation of vortex-nucleated magnetization reversal in individual ferromagnetic nanotubes

The reversal of a uniform axial magnetization in a ferromagnetic nanotube (FNT) has been predicted to nucleate and propagate through vortex domains forming at the ends. In dynamic cantilever magnetometry measurements of individual FNTs, we identify the entry of these vortices as a function of applied magnetic field and show that they mark the nucleation of magnetization reversal. We find that the entry field depends sensitively on the angle between the end surface of the FNT and the applied field. Micromagnetic simulations substantiate the experimental results and highlight the importance of the ends in determining the reversal process. The control over end vortex formation enabled by our findings is promising for the production of FNTs with tailored reversal properties.Comment: 20 pages, 13 figure

Nonlinear motion and mechanical mixing in as-grown GaAs nanowires

We report nonlinear behavior in the motion of driven nanowire cantilevers. The nonlinearity can be described by the Duffing equation and is used to demonstrate mechanical mixing of two distinct excitation frequencies. Furthermore, we demonstrate that the nonlinearity can be used to amplify a signal at a frequency close to the mechanical resonance of the nanowire oscillator. Up to 26 dB of amplitude gain are demonstrated in this way