arXiv subjects

J. Debosscher

Publications and source records attributed to J. Debosscher.

43 records · Page 3Linked to original sources

arXiv2009

CoRoT's view of newly discovered B-star pulsators: results for 358 candidate B pulsators from the initial run's exoplanet field data

We search for new variable B-type pulsators in the CoRoT data assembled primarily for planet detection, as part of CoRoT's Additional Programme. We aim to explore the properties of newly discovered B-type pulsators from the uninterrupted CoRoT space-based photometry and to compare them with known members of the Beta Cep and slowly pulsating B star (SPB) classes. We developed automated data analysis tools that include algorithms for jump correction, light-curve detrending, frequency detection, frequency combination search, and for frequency and period spacing searches. Besides numerous new, classical, slowly pulsating B stars, we find evidence for a new class of low-amplitude B-type pulsators between the SPB and Delta Sct instability strips, with a very broad range of frequencies and low amplitudes, as well as several slowly pulsating B stars with residual excess power at frequencies typically a factor three above their expected g-mode frequencies. The frequency data we obtained for numerous new B-type pulsators represent an appropriate starting point for further theoretical analyses of these stars, once their effective temperature, gravity, rotation velocity, and abundances will be derived spectroscopically in the framework of an ongoing FLAMES survey at the VLT.

astro-ph.SR↗

arXiv2009

Comparative clustering analysis of variable stars in the Hipparcos, OGLE Large Magellanic Cloud and CoRoT exoplanet databases

Context. Discovery of new variability classes in large surveys using multivariate statistics techniques such as clustering, relies heavily on the correct understanding of the distribution of known classes as point processes in parameter space. Aims. Our objective is to analyze the correspondence between the classical stellar variability types and the clusters found in the distribution of light curve parameters and colour indices of stars in the CoRoT exoplanet sample. The final aim is to help in the identification on new types of variability by first identifying the well known variables in the CoRoT sample. Methods. We apply unsupervised classification algorithms to identify clusters of variable stars from modes of the probability density distribution. We use reference variability databases (Hipparcos and OGLE) as a framework to calibrate the clustering methodology. Furthermore, we use the results from supervised classification methods to interpret the resulting clusters. Results.We interpret the clusters in the Hipparcos and OGLE LMC databases in terms of large-amplitude radial pulsators in the classical instability strip and of various types of eclipsing binaries. The Hipparcos data also provide clear distributions for low-amplitude nonradial pulsators. We show that the preselection of targets for the CoRoT exoplanet programme results in a completely different probability density landscape than the OGLE data, the interpretation of which involves mainly classes of low-amplitude variability in main-sequence stars. Our findings will be incorporated to improve the supervised classification used in the CoRoT catalogue production, once the existence of new classes or subtypes will be confirmed from complementary spectroscopic observations.

astro-ph.SR↗

arXiv2009

Long-term photometric monitoring with the Mercator telescope. Frequencies and multicolour amplitudes of $γ$ Doradus stars

Gamma Doradus stars are excellent targets for asteroseismology since the gravity modes present in these stars probe the deep stellar interiors. Mode identification will improve the knowledge of these stars considerably. A selected group of Gamma Doradus stars and some candidates were observed with the Mercator telescope to find and/or confirm the periodicities in the light variations and to derive reliable amplitude ratios in different pass bands. A frequency analysis was performed on all new data obtained in the Geneva photometric system. In order to have more reliable and accurate frequencies, the new data were combined with similar data from the literature and with Hipparcos observations. A set of frequencies that minimized the the residuals in a harmonic fit was searched for while allowing means and amplitudes to vary from one observation set to another. Frequencies and amplitudes in the photometric passbands of the Geneva system are given for 21 Gamma Doradus stars. We report the discovery of HD 74504 as a newly found Gamma Doradus star. Our study provides the first extensive multicolour database for the understanding of gravity modes in F-type stars.

astro-ph.GA↗

arXiv2009

Variability type classification of multi-epoch surveys

The classification of time series from photometric large scale surveys into variability types and the description of their properties is difficult for various reasons including but not limited to the irregular sampling, the usually few available photometric bands, and the diversity of variable objects. Furthermore, it can be seen that different physical processes may sometimes produce similar behavior which may end up to be represented as same models. In this article we will also be presenting our approach for processing the data resulting from the Gaia space mission. The approach may be classified into following three broader categories: supervised classification, unsupervised classifications, and "so-called" extractor methods i.e. algorithms that are specialized for particular type of sources. The whole process of classification- from classification attribute extraction to actual classification- is done in an automated manner.

astro-ph.IM↗

arXiv2009

Photometric campaign on massive stars in the open cluster NGC 5617

A campaign on the open cluster NGC 5617 was organized in order to characterize the pulsations and to better understand the internal structure of its stars. The variability of the cluster members was never studied before. We present the observations taken and an up-to-date analysis of the obtained time series, especially of several SPB candidates we discovered.

astro-ph.SR↗

arXiv2008

Automated supervised classification of variable stars II. Application to the OGLE database

We aim to extend and test the classifiers presented in a previous work against an independent dataset. We complement the assessment of the validity of the classifiers by applying them to the set of OGLE light curves treated as variable objects of unknown class. The results are compared to published classification results based on the so-called extractor methods.Two complementary analyses are carried out in parallel. In both cases, the original time series of OGLE observations of the Galactic bulge and Magellanic Clouds are processed in order to identify and characterize the frequency components. In the first approach, the classifiers are applied to the data and the results analyzed in terms of systematic errors and differences between the definition samples in the training set and in the extractor rules. In the second approach, the original classifiers are extended with colour information and, again, applied to OGLE light curves. We have constructed a classification system that can process huge amounts of time series in negligible time and provide reliable samples of the main variability classes. We have evaluated its strengths and weaknesses and provide potential users of the classifier with a detailed description of its characteristics to aid in the interpretation of classification results. Finally, we apply the classifiers to obtain object samples of classes not previously studied in the OGLE database and analyse the results. We pay specific attention to the B-stars in the samples, as their pulsations are strongly dependent on metallicity.

astro-ph↗

arXiv2007

Automated supervised classification of variable stars I. Methodology

The fast classification of new variable stars is an important step in making them available for further research. Selection of science targets from large databases is much more efficient if they have been classified first. Defining the classes in terms of physical parameters is also important to get an unbiased statistical view on the variability mechanisms and the borders of instability strips. Our goal is twofold: provide an overview of the stellar variability classes that are presently known, in terms of some relevant stellar parameters; use the class descriptions obtained as the basis for an automated `supervised classification' of large databases. Such automated classification will compare and assign new objects to a set of pre-defined variability training classes. For every variability class, a literature search was performed to find as many well-known member stars as possible, or a considerable subset if too many were present. Next, we searched on-line and private databases for their light curves in the visible band and performed period analysis and harmonic fitting. The derived light curve parameters are used to describe the classes and define the training classifiers. We compared the performance of different classifiers in terms of percentage of correct identification, of confusion among classes and of computation time. We describe how well the classes can be separated using the proposed set of parameters and how future improvements can be made, based on new large databases such as the light curves to be assembled by the CoRoT and Kepler space missions.

astro-ph↗