1. GSC-II Classifications Oct 2000 Annual Meeting V. Laidler G. Hawkins, R. White, R. Smart, A. Rosenberg, A. Spagna

2. 19 October 2000Classification / Laidler2 Preliminary Classification Goal: Classify as well as possible to plate limit Metric: Minimize overall number of errors Procedure: Use ranks to handle plate to plate variation Match training population to sky population OC1 oblique decision tree (Murthy et al) Build several decision trees & let them vote Classification categories star / nonstar / defect

3. 19 October 2000Classification / Laidler3 Next Step Classification Goal: Provide reliable guide stars to V~19(?) Metric: Minimize contamination of “stars” to V lim while maintaining sufficient completeness for adequate coverage Contamination: We called it a star but it’s really nonstellar Completeness: Everything that is really a star is called a star

4. 19 October 2000Classification / Laidler4 Development Areas Multi-plate weighted voting Training set magnitude distribution Training set sources Classification categories Classification features Object selection Available In progress Future

5. 19 October 2000Classification / Laidler5 Multi-plate Weighted Voting Weights calculated empirically from percentages of misclassifications (NED, NPM, ~4 plates per survey) Compensates for observed bias in classifier and breaks ties Weights star0.38 nonstar0.61 defect0.01 Voting Example starnonstardefectfinal 110nonstar 210star 321nonstar

6. 19 October 2000Classification / Laidler6 MP weighted voting compared to Mendez Galaxy model  Current classification comes from a single plate  Multiplate weighted voting is straightforward DB operation Conservative star selection further reduces contamination; coverage remains adequate

7. 19 October 2000Classification / Laidler7 Training set mag distribution: What happens to V < V lim objects? Preliminary approach occupy 20% of ranked hyperspace are outnumbered when counting errors Optimized approach have more dynamic range contribute all the weight when counting errors contain the same classification bias as the sky are free of classification bias

8. 19 October 2000Classification / Laidler8 Training Set Sources Classification Categories Decision trees can be improved by using training sets with smaller dispersion in parameter space Catalog objects will likely provide cleaner, better separated populations Galaxies and blends are different => reside in different areas of parameter space => individually constitute better defined populations than when combined  Galaxy / blend classifications are value added to the catalog

9. 19 October 2000Classification / Laidler9 New training set Magnitude balanced to F=17: bright only Star/galaxy/blend classifications Stars, galaxies from catalogs NED,NPM,CAMC,LCRS Blends from deblender “parent” objects 1200 objects XP330, XP853, XP005 b={48,41,28}

10. 19 October 2000Classification / Laidler10 New training set: Compare to production classifier “Above all, do no harm” Visually examine objects that changed classifications True Old (s/n/d) New (s/g/b) StarNonstarStarBetter GalaxyStar Galaxy Better Blend Star Galaxy Better Blend Star Galaxy Worse Blend Galaxy NonstarStarWorse Blend Galaxy Nonstar Blend Even BlendGalaxy

11. 19 October 2000Classification / Laidler11 New training set: compare to external catalogs Significant improvement in magnitude range of training set Extend training set: can we extend this performance to V lim ? Possibly use star/galaxy/blend to V lim, star/nonstar/defect below

12. 19 October 2000Classification / Laidler12 Future work: Classification features The “curse of dimensionality” tells us that tree performance can be improved by reducing the number of features Edinburgh group has used two features specifically to separate blends from galaxies Current classification features Maximum Density Integrated Density Semimajor axis Semiminor axis Ellipticity Unweighted semimajor axis Unweighted semiminor axis Unweighted ellipticity 4 texture features 2 spike features 16 areas

13. 19 October 2000Classification / Laidler13 Future work: Object Selection Object selection can be considered an additional classification step Select based on: Blend status Multi plate information Probability Select for functional or science goals: Minimize contamination Maximize completeness  Probability comes from leaf population  Final probability comes from averaging probabilities from each tree  Can we use probabilities to further optimize guide star selection?

14. 19 October 2000Classification / Laidler14 What do the probabilities mean? Do the probabilities measure the observed population? No. This is not unexpected. Decision trees are optimized to produce correct answers, not to produce accurate models of the probability function. Do the probabilities indicate reliability? Yes. Conclusion: We can use the probabilities to construct a “class quality” field, but should not take them at face value. Visual check of ~100 objects with J<17 on XJ763 (b =-34) Probability (S/N/D)ObservedNonblends only.97/.03/0 star.89/.11/0.94/.06/0.85/.15/0 star.60/.40/0.41/.59/0.13/.84/.03 nonstar.60/.34/.06.61/.32/.07

15. 19 October 2000Classification / Laidler15 How to Improve a Classifier PresentCurrentFuture Training set Tree construction Single plate voting Multi plate voting Object selection

16. 19 October 2000Classification / Laidler16

17. 19 October 2000Classification / Laidler17 Using Ranks Sort the objects in order by the raw feature Assign a ranked feature based on position in the list