Transcript Bus & Binzel 2002
Slide 1
Taxonomy of Small Bodies
AS3141 Benda Kecil dalam Tata Surya
Prodi Astronomi 2007/2008
B. Dermawan
Slide 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)
Slide 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
(S3OS2: ongoing >800 asteroids; Lazzaro et al. 2001)
Spectroscopy visible-wavelength spectroscopy
Slide 4
Spectroscopy
Bus et al. 2002
Extraction of one-dimensional spectra
Preprocessing of the CCD images
Calibration of the extracted spectra
Normalization to a solar-analog star
Slide 5
Bus & Binzel 2002
ECAS Colors &
SMASSII Spectra
Slide 6
Object’s Surface Material
Different
surface
material
on Vesta
0.506-m Fe2+ pyroxene
presence of Ca-rich
Slide 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
Slide 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)
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
Statistically significant boundaries exist between clusters of objects
Slide 9
SMASSII Taxonomy: basics
Bus et al. 2002
• 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.
Slide 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
Slide 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
ri 1.0 (i 0.55)
ri : 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
Slide 12
Bus & Binzel 2002
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’
Slide 13
SMASSII Taxonomy: separating the spectra
Bus & Binzel 2002
Slide 14
SMASSII Taxonomy: S-, C-, X-complex spectra
Bus & Binzel 2002
Slide 15
SMASSII Taxonomy: comparison & distribution
Bus & Binzel 2002
Bus & Binzel 2002
Slide 16
SMASSII Taxonomy: Result Table
Bus & Binzel 2002
Slide 17
SMASSII Taxonomy: description
Bus et al. 2002
Slide 18
Cont’d
Bus et al. 2002
Slide 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
Slide 20
TNOs & Centaurs Taxonomy (1)
TNOs
Centaurs
Lazzarin et al. 2003
Slide 21
TNOs & Centaurs Taxonomy (2)
Lazzarin et al. 2003
Slide 22
NEAs Taxonomy (1)
Binzel et al. 2002
Slide 23
Binzel et al. 2002
NEAs Taxonomy (2)
Binzel et al. 2002
Slide 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
o Data calibration is complicated
o Scaling telluric features a model of atmospheric
transmission (ATRAN, Lord 1992)
Slide 25
Visible & NIR Spectroscopy
– 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
0.7
Slide 26
SMASSII Taxonomy: spectra
Bus & Binzel 2002
Taxonomy of Small Bodies
AS3141 Benda Kecil dalam Tata Surya
Prodi Astronomi 2007/2008
B. Dermawan
Slide 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)
Slide 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
(S3OS2: ongoing >800 asteroids; Lazzaro et al. 2001)
Spectroscopy visible-wavelength spectroscopy
Slide 4
Spectroscopy
Bus et al. 2002
Extraction of one-dimensional spectra
Preprocessing of the CCD images
Calibration of the extracted spectra
Normalization to a solar-analog star
Slide 5
Bus & Binzel 2002
ECAS Colors &
SMASSII Spectra
Slide 6
Object’s Surface Material
Different
surface
material
on Vesta
0.506-m Fe2+ pyroxene
presence of Ca-rich
Slide 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
Slide 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)
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
Statistically significant boundaries exist between clusters of objects
Slide 9
SMASSII Taxonomy: basics
Bus et al. 2002
• 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.
Slide 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
Slide 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
ri 1.0 (i 0.55)
ri : 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
Slide 12
Bus & Binzel 2002
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’
Slide 13
SMASSII Taxonomy: separating the spectra
Bus & Binzel 2002
Slide 14
SMASSII Taxonomy: S-, C-, X-complex spectra
Bus & Binzel 2002
Slide 15
SMASSII Taxonomy: comparison & distribution
Bus & Binzel 2002
Bus & Binzel 2002
Slide 16
SMASSII Taxonomy: Result Table
Bus & Binzel 2002
Slide 17
SMASSII Taxonomy: description
Bus et al. 2002
Slide 18
Cont’d
Bus et al. 2002
Slide 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
Slide 20
TNOs & Centaurs Taxonomy (1)
TNOs
Centaurs
Lazzarin et al. 2003
Slide 21
TNOs & Centaurs Taxonomy (2)
Lazzarin et al. 2003
Slide 22
NEAs Taxonomy (1)
Binzel et al. 2002
Slide 23
Binzel et al. 2002
NEAs Taxonomy (2)
Binzel et al. 2002
Slide 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
o Data calibration is complicated
o Scaling telluric features a model of atmospheric
transmission (ATRAN, Lord 1992)
Slide 25
Visible & NIR Spectroscopy
– 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
0.7
Slide 26
SMASSII Taxonomy: spectra
Bus & Binzel 2002