The variability processing and analysis of the Gaia mission

We present the variability processing and analysis that is foreseen for the Gaia mission within Coordination Unit 7 (CU7) of the Gaia Data Processing and Analysis Consortium (DPAC). A top level description of the tasks is given.Comment: 4 pages, 1 figure. To be published in the proceedings of the GREAT-ITN conference "The Milky Way Unravelled by Gaia: GREAT Science from the Gaia Data Releases", 1-5 December 2014, University of Barcelona, Spain, EAS Publications Series, eds Nicholas Walton, Francesca Figueras, and Caroline Soubira

Creating a hierarchy of mental health stigma: testing the effect of psychiatric diagnosis on stigma

Levels of mental health stigma experienced can vary as a function of the presenting mental health problem (e.g. diagnosis and symptoms). However, these studies are limited because they exclusively use pairwise comparisons. A more comprehensive examination of diagnosis-specific stigma is needed. The aim of our study was to determine how levels of mental health stigma vary in relation to a number of psychiatric diagnoses, and identify what attributions predict levels of diagnosis-specific stigma. We conducted an online survey with members of the public. Participants were assessed in terms of how much stigma they had, and their attributions toward, nine different case vignettes, each describing a different mental health diagnosis. We recruited 665 participants. After controlling for social desirability bias and key demographic variables, we found that mental health stigma varied in relation to psychiatric diagnosis. Schizophrenia and antisocial personality disorder were the most stigmatised diagnoses, and depression, generalised anxiety disorder and obsessive-compulsive disorder were the least stigmatised diagnoses. No single attribution predicted stigma across diagnoses, but fear was the most consistent predictor. Assessing mental health stigma as a single concept masks significant between-diagnosis variability. Anti-stigma campaigns are likely to be most successful if they target fearful attributions

Pulsating star research and the Gaia revolution

In this article we present an overview of the ESA Gaia mission and of the unprecedented impact that Gaia will have on the field of variable star research. We summarise the contents and impact of the first Gaia data release on the description of variability phenomena, with particular emphasis on pulsating star research. The Tycho-Gaia astrometric solution, although limited to 2.1 million stars, has been used in many studies related to pulsating stars. Furthermore a set of 3,194 Cepheids and RR Lyrae stars with their times series have been released. Finally we present the plans for the ongoing study of variable phenomena with Gaia and highlight some of the possible impacts of the second data release on variable, and specifically, pulsating stars.Comment: 12 pages, 4 figures, proceedings for the 22nd Los Alamos Stellar Pulsation Conference Series Meeting "Wide field variability surveys: a 21st-century perspective", held in San Pedro de Atacama, Chile, Nov. 28 - Dec. 2, 201

“That little doorway where I could suddenly start shouting out”: barriers and enablers to the disclosure of distressing voices

Hearing distressing voices is a key feature of psychosis. The time between voice onset and disclosure may be crucial as voices can grow in complexity. This study investigated barriers and enablers to early voice disclosure. Interviews with 20 voice hearers underwent Thematic Analysis. Beliefs about the effect of disclosure on self and others acted as a barrier and enabler to voices being discussed. Voice hearing awareness should be increased amongst young people, the public and care services. To support earlier disclosure measures need to increase skill amongst those likely to be disclosed to

A comparative study of four significance measures for periodicity detection in astronomical surveys

We study the problem of periodicity detection in massive data sets of photometric or radial velocity time series, as presented by ESA's Gaia mission. Periodicity detection hinges on the estimation of the false alarm probability of the extremum of the periodogram of the time series. We consider the problem of its estimation with two main issues in mind. First, for a given number of observations and signal-to-noise ratio, the rate of correct periodicity detections should be constant for all realized cadences of observations regardless of the observational time patterns, in order to avoid sky biases that are difficult to assess. Secondly, the computational loads should be kept feasible even for millions of time series. Using the Gaia case, we compare the FM method of Paltani and Schwarzenberg-Czerny, the Baluev method and the GEV method of Süveges, as well as a method for the direct estimation of a threshold. Three methods involve some unknown parameters, which are obtained by fitting a regression-type predictive model using easily obtainable covariates derived from observational time series. We conclude that the GEV and the Baluev methods both provide good solutions to the issues posed by a large-scale processing. The first of these yields the best scientific quality at the price of some moderately costly pre-processing. When this pre-processing is impossible for some reason (e.g. the computational costs are prohibitive or good regression models cannot be constructed), the Baluev method provides a computationally inexpensive alternative with slight biases in regions where time samplings exhibit strong aliase