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_at_mpia-hd.mpg.de

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Science with surveys

• Survey characteristics
• large numbers of objects (gt106)
• 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 ...

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GAIA Galaxy survey mission

• Composition, formation and evolution of our
  Galaxy
• High precision astrometry for distances and
  proper motions (10 ?as _at_ V15 ? 1 distance at
  1kpc)
• Observe entire sky down to V20 _at_ 0.10.5
  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 Vlt17
• Comparison to Hipparcos
• 10 000 objects, 100 precision, 11 mags deeper
• ESA mission, approved for launch in c. 2011

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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

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GAIA scientific payload

• High stability SiC structure
• Non-deployable 3-mirror telescopes
• Optical (200-1000nm)
• Two astrometric telescopes
• 1.7m0.7m, 0.60.6 FOV
• Spectroscopic telescope
• 0.75m0.7m, 14 FOV

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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

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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 Vlt17
• ? 1-10 km/s radial velocities, abundances
• 11 medium band filters for all objects
• ? object classification, physical parameters,
  extinction, absolute fluxes

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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)

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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)

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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

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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

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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

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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!