Iron oxide bands in the visible and near-infrared reflectance spectra of primitive asteroids

Details Iron oxide bands in the visible and near-infrared reflectance spectra of primitive asteroids Iron oxide bands in the visible and near-infrared reflectance spectra of primitive asteroids

Mar 1993

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1993lpi....24..715j&link_type=abstract

In Lunar and Planetary Inst., Twenty-Fourth Lunar and Planetary Science Conference. Part 2: G-M p 715-716 (SEE N94-16173 03-91)

Computer Science

3

Asteroids, Charge Transfer, Iron Oxides, Reflectance, Silicates, Solar Position, Spectral Reflectance, Charge Coupled Devices, Heating, Least Squares Method, Spectra

Scientific paper

High resolution reflectance spectra of primitive asteroids (C, P, and D class and associated subclasses) have commonly revealed an absorption feature centered at 0.7 microns attributed to an Fe(2+)-Fe(3+) charge transfer transition in iron oxides and/or oxidized iron in phyllosilicates. A smaller feature identified at 0.43 microns has been attributed to an Fe(3+) spin-forbidden transition in iron oxides. In the spectra of the two main-belt primitive asteroids 368 Haidea (D) and 877 Walkure (F), weak absorption features which were centered near the location of 0.60-0.65 microns and 0.80-0.90 microns prompted a search for features at these wavelengths and an attempt to identify their origin(s). The CCD reflectance spectra obtained between 1982-1992 were reviewed for similar absorption features located near these wavelengths. The spectra of asteroids in which these absorption features have been identified are shown. These spectra are plotted in order of increasing heliocentric distance. No division of the asteroids by class has been attempted here (although the absence of these features in the anhydrous S-class asteroids, many of which have presumably undergone full heating and differentiation should be noted). For this study, each spectrum was treated as a continuum with discrete absorption features superimposed on it. For each object, a linear least squares fit to the data points defined a simple linear continuum. The linear continuum was then divided into each spectrum, thus removing the sloped continuum and permitting the intercomparison of residual spectral features.

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