1. Taxonomy of Small Bodies AS3141 Benda Kecil dalam Tata Surya Prodi Astronomi 2007/2008 B. Dermawan

2. Spectroscopy: history (1) 1929: Photographic Spectra Visible spectrum of 0.39 – 0.47  m (Vesta; Bobrovnikoff 1929) 1970: Spectrophotometry Visible spectrum of 0.3 – 1.1  m (McCord et al. 1970; Chapman et al. 1971)  Strong absorption bands in the UV and near 1  m First rigorous asteroid taxonomy (Chapman et al. 1975)  asteroid mineralogy Mid-1980s: Spectrophotometry Surveys Eight-Color Asteroid Survey (ECAS, Zellner et al. 1985)  ~600 asteroids  Tholen taxonomy (Tholen 1984)

3. Spectroscopy: history (2) Spectrograph: Spectroscopic survey Low-albedo asteroid survey (115 asteroids; Sawyer 1991) First Phase of Small Main-belt Asteroid Spectroscopic Survey (SMASSI: 316 asteroids; Xu et al. 1995) Second Phase of Small Main-belt Asteroid Spectroscopic Survey (SMASSII: 1447 asteroids; Bus & Binzel 2002) Small Solar System Objects Spectroscopic Survey (S 3 OS 2 : ongoing >800 asteroids; Lazzaro et al. 2001) Spectroscopy  visible-wavelength spectroscopy

4. Spectroscopy Bus et al. 2002 Preprocessing of the CCD images Extraction of one-dimensional spectra Calibration of the extracted spectra Normalization to a solar-analog star

5. Bus & Binzel 2002 ECAS Colors & SMASSII Spectra

6. Object’s Surface Material Different surface material on Vesta 0.506-  m Fe 2+ pyroxene  presence of Ca-rich

7. Effects of Surface Properties Phase reddening: reddening of reflectance spectra with increased phase angle NIR Spectrometer to Eros: slope 8-12% over phase angles 0  -100  Space Weathering: darkening & reddening of asteroids’ surface e.g. Chapman 1996: Explaining the spectral mismatches between asteroids and meteorites Particle size Particulate regolith on the surface Temperature 120 K (Trojans) to >300 K (NEAs) Shapes of spectral bands (olivines & pyroxenes) are sensitive to temperature

8. Taxonomy: methods Asteroid classification Bowell et al. 1978  Tholen & Barucci 1989 Data sets: - ECAS (Zellner etl al. 1985) - IRAS albedo (Veeder et al. 1989, Tedesco et al. 1992) Statistically significant boundaries exist between clusters of objects 1.Tholen taxonomy (1984): spanning tree clustering algorithm 2.Barucci et al. taxonomy (1987): G-mode analysis 3.Tedesco et al. taxonomy (1989): visual identification of groupings in a parameter space (two asteroid colors & IRAS albedo) 4.Howell et al taxonomy (1994): artificial neural network

9. Tholen taxonomy was utilized in an attempt to preserve the historic structure and spirit of past asteroid taxonomies Classes were defined solely on the presence (or absence) of absorption features contained in the visible-wavelength spectra The classes were arranged in a way that reflects the spectral continuum revealed by the SMASSII data Different analytical and multivariate analysis technique were used to properly parameterize the various spectral features. Labels of some class were based on human judgment. When possible, the sizes (scale-lengths) and boundaries of the taxonomic classes were defined based on the spectral variance observed in natural groupings among the asteroids. SMASSII Taxonomy: basics Bus et al. 2002

10. SMASSII Taxonomy: method Parameterization Principle Component Analysis (PCA)  Multivariate Analysis Techniques Maps Multivariate data into a new space whose axes are oriented in a way that best represents the data’s total variance In principal component space: - The first component (PC1): largest possible fraction of the variance in the data set. - PC2: the next largest fractions of the variance  Cluster together in groups that are well separated in some parameter space

11. SMASSII Taxonomy: spectral slope A.Extracted & calibrated spectrum B.Smoothing spline fit C.Linear least squares fit  slope parameter  D.Residual spectrum after division by the slope function r i : The relative reflectance at each channel I : The wavelength of the channel in microns  : The slope of the fitted line (unity at 0.55  m) Bus & Binzel 2002

12. SMASSII Taxonomy: PC 1.Spectra are essentially linear or featureless 2.Spectra contain a 1-  m absorption feature The two different loci corresponds to spectra with and without a 1-  m silicate absorption feature PC1  Slope  remove PC2  PC2’ PC3  PC3’ Bus & Binzel 2002

13. SMASSII Taxonomy: separating the spectra Bus & Binzel 2002

14. SMASSII Taxonomy: S-, C-, X-complex spectra Bus & Binzel 2002

15. SMASSII Taxonomy: comparison & distribution Bus & Binzel 2002

16. SMASSII Taxonomy: Result Table Bus & Binzel 2002

17. SMASSII Taxonomy: description Bus et al. 2002

18. Cont’d Bus et al. 2002

19. SMASSII Taxonomy: drawbacks  Can be cumbersome for newly observed asteroids  Allow for the classification of individual objects  The classification assigned to an asteroid is only as good as the observational data Variations in spectrum may change the taxonomic label

20. TNOs Centaurs TNOs & Centaurs Taxonomy (1) Lazzarin et al. 2003

21. TNOs & Centaurs Taxonomy (2) Lazzarin et al. 2003

22. NEAs Taxonomy (1) Binzel et al. 2002

23. NEAs Taxonomy (2) Binzel et al. 2002

24. Near-Infrared Spectroscopy NIR: ~1 – 4  m contains absorption bands that are fundamental to studies of mineralogy (Gaffey et al. 1989)  Hodapp (2000): high-quality asteroid spectra out to 2.5  m and beyond  Rayner et al. (1998): low- to medium-resolution NIR spectrograph & imager (SpeX) in IRTF oData calibration is complicated oScaling telluric features a model of atmospheric transmission (ATRAN, Lord 1992)

25. Visible & NIR Spectroscopy  0.7 – 2.5  m: silicate minerals (pyroxenes, olivines and plagioclase) Absorption bands near 1 & 2  m  2.5 – 3.5  m: hydrated minerals (bound water and structural OH) Absorption bands centered near 3  m

26. SMASSII Taxonomy: spectra Bus & Binzel 2002