1 / 13

Object classification and physical parametrization with GAIA and other large surveys

This article discusses the use of GAIA and other large surveys for object classification and physical parametrization, with a focus on the GAIA Galaxy survey mission. It covers survey characteristics, goals, GAIA satellite and mission details, GAIA scientific payload, and classification goals for GAIA. It also explores classification and parametrization principles, such as probabilistic methods, neural networks, and minimum distance methods, and addresses challenges faced in large, deep surveys.

emcnichols
Download Presentation

Object classification and physical parametrization with GAIA and other large surveys

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  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!

More Related