Classification of GAIA data

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Classification of GAIA data

• Coryn A.L. Bailer-Jones
• Max-Planck-Institut für Astronomie, Heidelberg
• calj_at_mpia.de

Overview GAIA classification objectives and
available data Approaches to classification
principles and problems Example classification
using RVS-like data Some specific issues Summary
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GAIA classification objectives

• discrete classification of objects
• as star, galaxy, quasar, solar system object,
  supernovae etc.
• determination of astrophysical parameters (APs)
  for stars
• Teff, logg, Fe/H, ?/Fe, CNO, A(?), Vrot,
  Vrad, activity
• combination with parallax to determine stellar
• luminosity, radius, (mass, age)
• identification of unresolved binaries (and
  parametrization of components where possible)
• efficient identification of new types of objects

Goal catalogue of object classifications and
astrophysical parameters
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GAIA data

• BBP 4 broad band filters all objects
• MBP 10-20 medium band filters all objects
• ? object classification stellar Teff, logg,
  Fe/H, A(?)
• RVS 849-874 nm spectrum, 0.04 nm/pixel Glt17
• ? stellar Vrad, Vrot, specific element
  abundances
• Astrometry ? parallax, kinematics, unresolved
  binaries
• Time domain ? 50 epochs over 5 years
  (photometric variability)
• ? Inhomogeneous data

Redshiftproblem to get RV, need correct SpT
template, but to determine SpT (may) need to
know ? shift ? use MBP data to give SpT and
iterate Generally use MBP data to give initial
classification of RVS data
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Classification principles

• Supervised approach
• use pre-classified data (templates) to infer the
  desired mapping
• apply mapping to any new data to give APs or
  classes
• But, the desired mapping is generally
  degenerate...

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Minimum Distance Methods (MDMs)

• Search for nearest neighbours (templates) in data
  space
• Assign parameters according to these
• Generally interpolate
• either in data space ? f(d w)
• or in parameter space D g(? w)

• Need to scale data dimensions
• e.g. k-nn, ?2 min, cross-correlation
• a local classification method

? astrophysical parameter(s) d1,d2
data D distance to a template
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Classification principles

• selecting just local neighbours in data space can
  lead to systematic errors or missed solutions
• need to find global (forward) mapping and
  identify degenerate regions
• more complex in higher dimensional spaces (data
  or parameters)
• severity of degeneracy depends upon the density
  of template grid and noise in the data

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Artificial Neural Networks (ANNs)

• Functional mapping astrophysical parameters
  f(data weights)
• Weights determined by training on pre-classified
  data (templates)
• ? least squares minimization of total
  classification error (numerical methods)
• ? global interpolation of data

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Classification example with high-res spectra

• Database of 611 real stellar spectra from Cenarro
  et al. (2001)
• variation over Teff, logg, Fe/H
• coverage 849 - 874 nm (same as GAIA RVS)
• resolution 0.15 nm _at_ 0.075 nm/pixel (poorer
  than GAIA?)
• SNR median70 90 in range 20-140
• Randomly split data set into two sets
• train a neural network on one set and test its
  performance on the other.

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Distribution over APs in Cenarro et al. data
blue training data (300) red test data (311)
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Results Teff and logg
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Results Fe/H
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Requirements of the classification scheme

• produce both discrete classification and
  continuous parametrization (e.g. star vs. quasar,
  APs of stars)
• recognition of degeneracies in presence of noise
• (i.e. recognise multiple classifications for
  given data vector)
• robustly handle missing and censored data
• possible RVS lossy compression (as function of
  magnitude)
• ? handle different amounts/formats of data
• reliable determination of parametrization
  uncertainties
• accommodate ever-improving stellar models
• all this for a very wide range of type of objects
  ...

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Classification schemes
P probability APs astrophysical parameters
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Model training

• Real spectra and synthetic spectra not identical
• systematic differences (modelling uncertainties,
  e.g. opacities)
• increased cosmic scatter in real spectra
  (unaccounted-for APs)

1. Can synthetic spectra be used to reliably
parametrize GAIA data? 2. Are performances
representative of what can be achieved? 3. Do
synthetic spectra give the best optimization of
phot/spec systems?
23 require accurate synthetic spectra (or large
set of real spectra)

• Can overcome mismatch problem for (1)
• use real GAIA data of pre-selected targets to
  apply corrections to synthetic SEDs
• APs of these targets determined from higher
  resolution spectra from ground-based spectra

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Summary

• classification with GAIA data is a challenging
  problem
• methods used so far in (astronomical)
  classification literature are suboptimal for this
  purpose
• ? further development of methods is a high
  priority
• particular problems to overcome are
• - degeneracy (especially with MBP data and
  compressed RVS data)
• - inhomogeneous data
• development of classification methods is very
  dependent on appropriate data (real or synthetic)
• - both of targets of interest
• - and of contaminating objects

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ICAP the GAIA classification working group

• WG responsible for addressing classification
  issues for GAIA
• 14 core members 17 associate members

GAIA Classification meeting 2-3
December Heidelberg, Germany Anyone interested
in classification issues broadly related to GAIA
is welcome to attend http//www.mpia.de/GAIA/
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Use of astrometric data in classification

• Parallax and proper motion very important for
• identifying quasars
• identifying solar system objects (NEOs,
  asteroids, KBOs)
• determining stellar properties (gives radius and
  helps binarity detection)

However, proper motion and Galactic position
should not be used to help determine stellar
parameters ? introduces biases based on current
knowledge of the Galaxy ? understanding
relationship between kinematics and intrinsic
stellar parameters is key GAIA science goal