Object classification andphysical parametrization withGAIA and other large surveys

Coryn A.L. Bailer-JonesMax-Planck-Institut für Astronomie, Heidelberg

calj@mpia-hd.mpg.de

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 ...

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

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 (200-1000nm)• 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 (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

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)

Classification/Parametrization Principles

Partition multidimensional data space to:1. classify objects into known classes2. parametrize objects on continuous physical scalesAssign 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 methods2. 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)

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? (109 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 databroad 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!