On the nature of the fast moving star S2 in the Galactic Center

We analyze the properties of the star S2 orbiting the supermassive black hole at the center of the Galaxy. A high quality SINFONI H and K band spectrum obtained from coadding 23.5 hours of observation between 2004 and 2007 reveals that S2 is an early B dwarf (B0-2.5V). Using model atmospheres, we constrain its stellar and wind properties. We show that S2 is a genuine massive star, and not the core of a stripped giant star as sometimes speculated to resolve the problem of star formation so close to the supermassive black hole. We give an upper limit on its mass loss rate, and show that it is He enriched, possibly because of the presence of a magnetic field.Comment: 4 pages, 5 figures, ApJ letters accepte

Spectroscopy of mechanical dissipation in micro-mechanical membranes

We measure the frequency dependence of the mechanical quality factor (Q) of SiN membrane oscillators and observe a resonant variation of Q by more than two orders of magnitude. The frequency of the fundamental mechanical mode is tuned reversibly by up to 40% through local heating with a laser. Several distinct resonances in Q are observed that can be explained by coupling to membrane frame modes. Away from the resonances, the background Q is independent of frequency and temperature in the measured range.Comment: 4 pages, 5 figure

Seizure characterisation using frequency-dependent multivariate dynamics

The characterisation of epileptic seizures assists in the design of targeted pharmaceutical seizure prevention techniques and pre-surgical evaluations. In this paper, we expand on recent use of multivariate techniques to study the crosscorrelation dynamics between electroencephalographic (EEG) channels. The Maximum Overlap Discrete Wavelet Transform (MODWT) is applied in order to separate the EEG channels into their underlying frequencies. The dynamics of the cross-correlation matrix between channels, at each frequency, are then analysed in terms of the eigenspectrum. By examination of the eigenspectrum, we show that it is possible to identify frequency dependent changes in the correlation structure between channels which may be indicative of seizure activity. The technique is applied to EEG epileptiform data and the results indicate that the correlation dynamics vary over time and frequency, with larger correlations between channels at high frequencies. Additionally, a redistribution of wavelet energy is found, with increased fractional energy demonstrating the relative importance of high frequencies during seizures. Dynamical changes also occur in both correlation and energy at lower frequencies during seizures, suggesting that monitoring frequency dependent correlation structure can characterise changes in EEG signals during these. Future work will involve the study of other large eigenvalues and inter-frequency correlations to determine additional seizure characteristics