Object classification and physical parametrization with GAIA and other large surveys Coryn A.L. Bailer-Jones Max-Planck-Institut für Astronomie, Heidelberg

Science with surveys Survey characteristics large numbers of objects (>10 6 ) 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 (T eff, logg, [Fe/H], etc.) efficient detection of new types of objects SDSS, LSST, VST/VISTA, DIVA, GAIA, virtual observatory...

GAIA Galaxy survey mission Composition, formation and evolution of our Galaxy High precision astrometry for distances and proper motions (10  V=15  1% distance at 1kpc) Observe entire sky down to 0.1–0.5´´ resolution  10 9 stars across all stellar populations quasars, 10 7 galaxies, 10 5 SNe, 10 6 SSOs Observe everything in 15 medium and broad band filters High resolution spectroscopy (for radial velocities) for V<17 Comparison to Hipparcos: × objects, ×100 precision, 11 mags deeper ESA mission, “approved” for launch in c. 2011

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

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

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

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 ( nm) for V<17  1-10 km/s radial velocities, abundances 11 medium band filters for all objects  object classification, physical parameters, extinction, absolute fluxes

Classification goals for GAIA Classification as star, galaxy, quasar, solar system objects etc. Determination of physical parameters of all stars - T eff, logg, [Fe/H], [  /Fe], CNO, A( ), V rot, V rad, 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)

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 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)

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

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? (10 9 in c. 1 week  1500/s)  = astrophysical parameter; d = data

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

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!