Transcript PPT - Max-Planck-Institut für Astronomie

Object classification and
physical parametrization with
GAIA and other large surveys
Coryn A.L. Bailer-Jones
Max-Planck-Institut für Astronomie, Heidelberg
[email protected]
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 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)
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, multidimensional 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!