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GSC2'2 Classification

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Title: GSC2'2 Classification


1
GSC2.2 Classification
  • GSC II Annual Meeting
  • October 2001

2
Single Plate Classification
  • Decision tree classifier
  • Use ranks to handle plate to plate variation
  • 5000 objects in training set
  • OC1 oblique decision tree (Murthy et al)
  • Build several decision trees let them vote
  • Classification categories star / nonstar / defect

3
GSC2.2 Classification
  • Unlike astrometry and photometry, where one best
    value was selected per object (per bandpass),
  • GSC2.2 classification can combine multiplate
    information to improve the final classifications,
  • And counter some known weaknesses.

4
MultiPlate Voting
  • For each object
  • Collect all single-plate measurements
  • Even from plates not being exported, eg IV-N
  • Override defect-gtnonstar if N(obs)gt1
  • Matched objects likely to be real objects
  • Eliminate 25um scan data, if 15um data exist
  • Classifier poorly tuned for these scans
  • Majority vote of remaining measurements
  • Voting classifiers is known to improve results
  • Break ties in favor of nonstars
  • Compensates for known bias

5
Auxiliary Information the Source Status Flag
  • GSC2.2 provides a wealth of additional
    information about each object via the source
    status flag.
  • Much of this information is pertinent to the
    quality of the final classification.
  • Informed users can further optimize their results
    (eg, guide star selection) with this auxiliary
    data.

6
Status Flag Details 0987654321
  • 10 digit decimal mask with relevant info
  • Columns 0 blend status
  • 9 incomplete processing
  • 8 classification voters
  • 7 classification unanimity
  • 654 photometric details (V,J,F)
  • 3 centroider details
  • 21 number of plate observations


7
Classification and the Status Flag
  • 0 blend status
  • Poorly tuned for blends gt lower confidence
  • 9 incomplete processing
  • No features computed gt lower confidence
  • 8 classification voters
  • Multiple voters gt higher confidence
  • 25um voters gt lower confidence

8
Classification and the Status Flag
  • 7 classification unanimity
  • Unanimous vote gt higher confidence
  • 654 photometric details (V,F,J)
  • 3 centroider details
  • 21 number of plate observations
  • More voters gt higher confidence


9
Bright Objects
  • Tycho stars are included in the GSC2.2.
  • Classification was set to star for these objects
  • Status flag 9999999900 for Tycho stars
  • GSC1 data were omitted from the GSC2.2
  • Classifications were excluded from voting
  • GSC1 classifier superior for mlt14
  • Include GSC1 classification in next export

10
Evaluating Performance Not a simple problem
  • What to measure?
  • Correctness completeness contamination
  • Magnitude and latitude variations
  • What to compare against?
  • GSCII was constructed because there is nothing
    comparable to it!
  • Nonstar ltgt galaxy
  • Automatically classified samples are less
    reliable
  • Visually classified samples are few and small

11
NPM/SPM Starsvs magnitude latitude
12
NPM/SPM Galaxiesvs magnitude latitude
13
SDSS Stars and Galaxiesvs magnitude
14
Accuracy vs the real questions
  • How complete is my sample of nonstars?
  • How pure is my sample of stars?
  • What is the probability that the GSC2.2
    classification of this object is correct?
  • The answers depend on your sample, as well as on
    the properties of the catalog.
  • A single quoted accuracy does not suffice.

15
Accuracy vs the real questions
  • P(TsS) P(STs)P(Ts) / P(S)
  • This formulation is
  • Responsive to magnitude and latitude variations
  • Adaptable to a priori effects of sampling
  • Adaptable to your favorite galaxy model
  • Computable (we think! - in progress)
  • Answers the real questions.
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