In this paper we describe a methodology to be used for the automated analysis of the spectroscopic contents of the GAUDI archive. We perform chi-square minimisation in the 5000-5200 Å spectral window to infer the atmospheric parameters and their random internal uncertainties. Effective temperatures are anchored using a zero-point correction into

Within the context of the collaboration "B fields in OB stars" (BOB), we used the FORS2 low-resolution spectropolarimeter to search for a magnetic field in 50 massive stars, including two reference magnetic massive stars. Because of the many controversies of magnetic field detections obtained with the FORS instruments, we derived the magnetic field

Using the BaLROG (Bars in Low Redshift Optical Galaxies) sample of 16 morphologically distinct barred spirals, we constrain the influence of bars on nearby galaxies observationally. Our sample appears small compared to ongoing IFU surveys, but offers a tenfold sharper spatial resolution (˜100 pc) as each galaxy is a mosaic of several pointings

We describe results from our study of a sample of spiral galaxies of a wide range of Hubble types on the basis of near-IR imaging obtained with INGRID on the WHT. We focus on the determination of bar torques, or bar strengths, from our images, and show that this bar strength only very weakly correlates with de Vaucouleurs bar type, or with bar axis

We use ACS data from the HST Treasury survey of the Coma cluster (z˜0.02) to study the properties of barred galaxies in the Coma core, the densest environment in the nearby Universe. This study provides a complementary data point for studies of barred galaxies as a function of redshift and environment. From ˜470 cluster members brighter than MI =

We perform a Bayesian parameter inference in the context of resonantly damped transverse coronal loop oscillations. The forward problem is solved in terms of parametric results for kink waves in one-dimensional flux tubes in the thin tube and thin boundary approximations. For the inverse problem, we adopt a Bayesian approach to infer the most