The VLT-FLAMES tarantula survey:XII. Rotational velocities of the single O-type stars

Context. The 30 Doradus (30 Dor) region of the Large Magellanic Cloud, also known as the Tarantula nebula, is the nearest starburst region. It contains the richest population of massive stars in the Local Group, and it is thus the best possible laboratory to investigate open questions on the formation and evolution of massive stars. Aims. Using ground-based multi-object optical spectroscopy obtained in the framework of the VLT-FLAMES Tarantula Survey (VFTS), we aim to establish the (projected) rotational velocity distribution for a sample of 216 presumably single O-type stars in 30 Dor. The sample is large enough to obtain statistically significant information and to search for variations among subpopulations - in terms of spectral type, luminosity class, and spatial location - in the field of view. Methods. We measured projected rotational velocities, 3e sin i, by means of a Fourier transform method and a profile fitting method applied to a set of isolated spectral lines. We also used an iterative deconvolution procedure to infer the probability density, P(3e), of the equatorial rotational velocity, 3e. Results. The distribution of 3e sin i shows a two-component structure: a peak around 80 km s1 and a high-velocity tail extending up to 600 km s^-1 This structure is also present in the inferred distribution P(3e) with around 80% of the sample having 0 <3e ≤ 300 km s^-1 and the other 20% distributed in the high-velocity region. The presence of the low-velocity peak is consistent with what has been found in other studies for late O- and early B-type stars. Conclusions. Most of the stars in our sample rotate with a rate less than 20% of their break-up velocity. For the bulk of the sample, mass loss in a stellar wind and/or envelope expansion is not efficient enough to significantly spin down these stars within the first few Myr of evolution. If massive-star formation results in stars rotating at birth with a large portion of their break-up velocities, an alternative braking mechanism, possibly magnetic fields, is thus required to explain the present-day rotational properties of the O-type stars in 30 Dor. The presence of a sizeable population of fast rotators is compatible with recent population synthesis computations that investigate the influence of binary evolution on the rotation rate of massive stars. Even though we have excluded stars that show significant radial velocity variations, our sample may have remained contaminated by post-interaction binary products. That the highvelocity tail may be populated primarily (and perhaps exclusively) by post-binary interaction products has important implications for the evolutionary origin of systems that produce gamma-ray bursts. © 2013 Author(s).

Joint Analysis of Multiple Algorithms and Performance Measures

There has been an increasing interest in the development of new methods using Pareto optimality to deal with multi-objective criteria (for example, accuracy and time complexity). Once one has developed an approach to a problem of interest, the problem is then how to compare it with the state of art. In machine learning, algorithms are typically evaluated by comparing their performance on different data sets by means of statistical tests. Standard tests used for this purpose are able to consider jointly neither performance measures nor multiple competitors at once. The aim of this paper is to resolve these issues by developing statistical procedures that are able to account for multiple competing measures at the same time and to compare multiple algorithms altogether. In particular, we develop two tests: a frequentist procedure based on the generalized likelihood-ratio test and a Bayesian procedure based on a multinomial-Dirichlet conjugate model. We further extend them by discovering conditional independences among measures to reduce the number of parameters of such models, as usually the number of studied cases is very reduced in such comparisons. Data from a comparison among general purpose classifiers is used to show a practical application of our tests.

Education as a Predictor of Antidepressant and Anxiolytic medication use after Bereavement: a population based record linkage study.

Purpose: Educational attainment has been shown to be positively associated with mental health and a potential buffer to stressful events. One stressful life event likely to affect everyone in their lifetime is bereavement. This paper assesses the effect of educational attainment on mental health post bereavement.
Methods: By utilising large administrative datasets, linking Census returns to death records and prescribed medication data, we analysed the bereavement exposure of 208,332 individuals aged 25-74 years. Two-level multi-level logistic regression models were constructed to determine the likelihood of antidepressant medication use (a proxy of mental ill-health) post bereavement given level of educational attainment.
Results: Individuals who are bereaved have greater antidepressant use than those who are not bereaved, with over a quarter (26.5%) of those bereaved by suicide in receipt of antidepressant medication compared to just 12.4% of those not bereaved. Within individuals bereaved by a sudden death those with a University Degree or higher qualifications are 73% less likely to be in receipt of antidepressant medication compared to those with no qualifications, after full adjustment for demographic, socio-economic and area factors (OR=0.27, 95% CI 0.09,0.75). Higher educational attainment and no qualifications have an equivalent effect for those bereaved by suicide.
Conclusions: Education may protect against poor mental health, as measured by the use of antidepressant medication, post bereavement, except in those bereaved by suicide. This is likely due to the improved cognitive, personal and psychological skills gained from time spent in education.