The meeting will have the form of a round table of Working Group objectives. The subjects will be briefly introduced, then the actions will be planned.
 
 

Variable Star Working Group topics

• Variable star characteristics ( Juan Fabregat on Be stars )
• Compilation of surveys (Jan Cuypers)
• Estimation of the number of variable stars observed (Giuseppe Bono)
• Study of other future surveys
• Study of classification (Vasily Belokurov) see Vasily's webpage
• Tools available
• New studies of sampling law
• Statistical tests
• Procedure for programme verification/validation

• Introduction of variability in Becancon Galactic model (fraction of variable stars)
• Comparison of "time scales"/period search methods
• Visualisation of data
• Follow up observations from the ground (Michel Boer)

ICAP Discussion topics, draft, CBJ 30.08.04

• 1. What kind of top-level classification hierarchy should we use for classification?
  • hierarchical vs. parallel (see ICAP-CBJ-007)
• 2. How to overcome the the problem of systematic errors in AP determination, in particular for the 'weak' APs, those which only weakly effect the photometric data (e.g. logg, [Fe/H])
• 3. Extension of parametrization methods to determining alpha element abundances
  • from photometry and/or spectroscopy?
  • which synthetic data can we use?
• 4. Determination of interstellar extinction
  • star-by-star or using a 3D Galactic extinction model?
  • a single extinction law (value of R_v) or distribution?
• 5. How to estimate uncertainties in APs derived from data
  • statistical estimates based on a validation set?
  • bootstrapping (ICAP-PW-004)?
  • parametrizing posterior probability distribution?
• 6. Machine learning methods for multidimensional classification and parametrization
  • neural networks
  • support vector machines
  • minimum distance / nearest neighbour methods
  • others? See ICAP-CH-001.
  • performance of various method (see ICAP-AB-004)
  • how to address the problem of degeneracy between APs (see ICAP-CBJ-007)
• 7. How do we calibrate the physical parametrization models? Do we rely entirely on synthetic spectra, or should these be adjusted based on real spectra? If the latter, how should this be done? (see ICAP-CBJ-002, ICAP-AB-002 and ICAP-CS-001)
• 8. We have focused on supervised parametrization schemes, i.e. determining physical parameters for stars directly from the data. These necessarily depend on specific stellar models. Should we also consider:
  • unsupervised schemes in order to find natural groupings in the data
  • empirical classification schemes, i.e. not directly going to physical parameters, but rather to MK system-like empirical parameters
• 9. Should we look also at morphological classification of image data, e.g. to identify extended objects (mostly galaxies)? This may be useful for onboard detection.
• 10. It is generally accepted that parallaxes should be included where available in the AP determination. How should these be introduced into the parametrization models? Should they rely on empirical or theorerical mass-luminosity-radius relations (or a mix)?
• 11. How should we combine photometric and spectroscopic data in the classifications?
  • keep them entirely separate, i.e. two independent determinations?
  • combine them on an equal footing, i.e. just expand the size of the input data space?
• 12. How do we best detect astrometrically-unresolved binary stars from their composite photometry? (We will have large numbers of such objects, so detecting them is important for mass function studies etc.) See ICAP-PW-003 and ICAP-PW-002.
• 13. How should we deal with peculiar stars?
  • identify them as "non-normal" (outliers) and ignore them?
  • attempt to classify them? If so, what classification/parametrization scheme?
• 14. Should we incorporate additional non-Gaia data into the classification for individual objects, such as 2MASS?