Оглавление
Изображения
Загрузить ваше изображение
DSS Images Other Images
Публикации по объекту
Bayesian inference of stellar parameters and interstellar extinction using parallaxes and multiband photometry Astrometric surveys provide the opportunity to measure the absolutemagnitudes of large numbers of stars, but only if the individualline-of-sight extinctions are known. Unfortunately, extinction is highlydegenerate with stellar effective temperature when estimated frombroad-band optical/infrared photometry. To address this problem, Iintroduce a Bayesian method for estimating the intrinsic parameters of astar and its line-of-sight extinction. It uses both photometry andparallaxes in a self-consistent manner in order to provide anon-parametric posterior probability distribution over the parameters.The method makes explicit use of domain knowledge by employing theHertzsprung-Russell Diagram (HRD) to constrain solutions and to ensurethat they respect stellar physics. I first demonstrate this method byusing it to estimate effective temperature and extinction from BVJHKdata for a set of artificially reddened Hipparcos stars, for whichaccurate effective temperatures have been estimated from high-resolutionspectroscopy. Using just the four colours, we see the expected strongdegeneracy (positive correlation) between the temperature andextinction. Introducing the parallax, apparent magnitude and the HRDreduces this degeneracy and improves both the precision (reduces theerror bars) and the accuracy of the parameter estimates, the latter byabout 35 per cent. The resulting accuracy is about 200 K in temperatureand 0.2 mag in extinction. I then apply the method to estimate theseparameters and absolute magnitudes for some 47 000 F, G, K Hipparcosstars which have been cross-matched with Two-Micron All-Sky Survey(2MASS). The method can easily be extended to incorporate the estimationof other parameters, in particular metallicity and surface gravity,making it particularly suitable for the analysis of the 109stars from Gaia.
| Lithium Depletion of Nearby Young Stellar Associations We estimate cluster ages from lithium depletion in fivepre-main-sequence groups found within 100 pc of the Sun: the TW Hydraeassociation, η Chamaeleontis cluster, β Pictoris moving group,Tucanae-Horologium association, and AB Doradus moving group. Wedetermine surface gravities, effective temperatures, and lithiumabundances for over 900 spectra through least-squares fitting tomodel-atmosphere spectra. For each group, we compare the dependence oflithium abundance on temperature with isochrones from pre-main-sequenceevolutionary tracks to obtain model-dependent ages. We find that theη Cha cluster and the TW Hydrae association are the youngest, withages of 12+/-6 Myr and 12+/-8 Myr, respectively, followed by the βPic moving group at 21+/-9 Myr, the Tucanae-Horologium association at27+/-11 Myr, and the AB Dor moving group at an age of at least 45 Myr(whereby we can only set a lower limit, since the models-unlike realstars-do not show much lithium depletion beyond this age). Here theordering is robust, but the precise ages depend on our choice of bothatmospheric and evolutionary models. As a result, while our ages areconsistent with estimates based on Hertzsprung-Russell isochrone fittingand dynamical expansion, they are not yet more precise. Our observationsdo show that with improved models, much stronger constraints should befeasible, as the intrinsic uncertainties, as measured from the scatterbetween measurements from different spectra of the same star, are verylow: around 10 K in effective temperature, 0.05 dex in surface gravity,and 0.03 dex in lithium abundance.
|
Добавить новую статью
Внешние ссылки
- - Внешних ссылок не найдено -
Добавить внешнюю ссылку
Группы:
|
Наблюдательные данные и астрометрия
Созвездие: | Жираф |
Прямое восхождение: | 06h54m54.09s |
Склонение: | +73°18'55.6" |
Видимая звёздная величина: | 9.668 |
Собственное движение RA: | -58.9 |
Собственное движение Dec: | -7.5 |
B-T magnitude: | 10.436 |
V-T magnitude: | 9.732 |
Каталоги и обозначения:
|