Object classification and physical parametrization with GAIA and other large surveys

1. Object classification andphysical parametrization withGAIA and other large surveys Coryn A.L. Bailer-Jones Max-Planck-Institut für Astronomie, Heidelberg calj@mpia-hd.mpg.de

2. Science with surveys Survey characteristics • large numbers of objects (>106) • no pre-selection  different types of objects (stars, galaxies, quasars, asteroids, etc.) • several observational ‘dimensions’ (e.g. filters, spectra) Goals • discrete classification of objects (star, galaxy; or stellar types) • continuous physical parametrization (Teff, logg, [Fe/H], etc.) • efficient detection of new types of objects SDSS, LSST, VST/VISTA, DIVA, GAIA, virtual observatory ...

3. GAIA Galaxy survey mission • Composition, formation and evolution of our Galaxy • High precision astrometry for distances and proper motions (10 as @ V=15  1% distance at 1kpc) • Observe entire sky down to V=20 @ 0.1–0.5´´ resolution  109 stars across all stellar populations + 105 quasars, 107 galaxies, 105 SNe, 106 SSOs • Observe everything in 15 medium and broad band filters • High resolution spectroscopy (for radial velocities) for V<17 • Comparison to Hipparcos: ×10 000 objects, ×100 precision, 11 mags deeper • ESA mission, “approved” for launch in c. 2011

4. GAIA satellite and mission • 8.5m × 2.9m (deployed sun shield) • 3100 kg (at launch) • Earth-Sun L2 Lissajous orbit • Continuously rotating (3hr period), precessing (80 days) and observing • 5 year mission • Each object observed c.100 times • Cost at completion: 570 MEuro

5. GAIA scientific payload • High stability SiC structure • Non-deployable 3-mirror telescopes • Optical (200-1000nm) • Two astrometric telescopes: 1.7m×0.7m, 0.6°×0.6° FOV • Spectroscopic telescope: 0.75m×0.7m, 1°×4° FOV

6. GAIA astrometric focal plane • CCDs clocked in TDI mode • 60cm × 70 cm, 250 CCDs, 2780 pixels × 2150 pixels • 21.5s crossing time • Star mappers: real-time onboard detection (only samples transmitted due to limited telemetry rate) • Main astrometric field: high precision centroiding (0.001 pix) from high SNR • Four broad band filters: chromatic correction

7. GAIA spectroscopic focal plane • Operates on same principle as astrometric field (independent star mappers) • Light dispersed in across-scan direction in central part of field: ~ 1Å resolution spectroscopy around CaII (850-875nm) for V<17  1-10 km/s radial velocities, abundances • 11 medium band filters for all objects  object classification, physical parameters, extinction, absolute fluxes

8. Classification goals for GAIA • Classification as star, galaxy, quasar, solar system objects etc. • Determination of physical parameters of all stars - Teff, logg, [Fe/H], [/Fe], CNO, A(), Vrot, Vrad, activity • Use all data (photometric, spectroscopic, astrometric) • Combine with parallax to determine stellar: - luminosity, radius, (mass, age) • Must be able to cope with: - unresolved binaries (help from astrometry) - photometric variability (can exploit: Cepheids, RR Lyrae) - redshifted objects - extended object (can deal with separately)

9. Classification/Parametrization Principles Partition multidimensional data space to: 1. classify objects into known classes 2. parametrize objects on continuous physical scales Assign classes/parameters in presence of noise Multiple 2-dimensional colour-colour diagrams inadequate! 1. direct probabilistic methods (Goebel et al. 1989; Christlieb et al. 1998) neural networks (Storrie-Lombardi et al. 1992; Odewahn et al. 1993) clustering methods 2. neural networks (Weaver & Torres-Dodgen 1995,1997; Singh et al. 1998 Bailer-Jones 1996,2000; Snider et al. 2001) MDM (Katz et al. 1998; Elsner et al. 1999; Vansevicius et al. 2002) Gaussian Processes (krigging) (Bailer-Jones et al. 1999)

10. Neural Networks (NNs) • Functional mapping: parameters = f(data; weights) • Weights determined by training on pre-classified data  least squares minimization of total classification error  global interpolation of data Problems: • local minima • training data distribution • missing and censored data

11. Minimum Distance Methods (MDMs) • Assign parameters according to nearest template(s) (k-nn, 2 min.) • Generally interpolate: either in data space:  = f(d; w) or in parameter space: D = g(; w)  new =  which minimizes D • Local methods Problems: • distance weighting • number of neighbours (bias/variance) • simultaneous determination of multiple parameters • speed? (109 in c. 1 week  1500/s)  = astrophysical parameter; d = data

12. Challenges for large, deep surveys General • interstellar extinction • photometric variability (pulsating stars, quasars) • multiple solutions (data degeneracy: noise dependent) • incorporation of prior information (iterative solutions) • robust to missing and censored data • known noise model: uncertainty predictions • template/training data: real vs. synthetic vs. mix Additional for GAIA (and DIVA) • unresolved binary stars (biases parameters) • use parallax information and local astrometry/RVs Most work to date has been on ‘cleaned’ (i.e. biased) data sets

13. Summary • Large, deep surveys produce complex, inhomogeneous, multi-dimensional datasets • Powerful, robust, automated methods for object classification and physical parametrization are required, but ... • ... many issues remain to be addressed • GAIA presents particular challenges: photometric, spectroscopic, astrometric and kinematic data broad science goals  wide range of objects to be classified • Discrete vs. continuous, local vs. global methods (NNs, MDMs, GPs, clustering methods) • Existing methods to be extended; new methods to be explored New members of GAIA Classification WG always welcome!