TOWARD A METEORITE-BASED ASTEROID TAXONOMY

The Bus-DeMeo (B-DM) asteroid taxonomy has revolutionized our ability to organize asteroid populations by making it possible to group similar objects together. This classification was initially based on slope values and principal component scores that were computed for the Small Main-belt Asteroid Spectroscopic Survey (SMASSII). Spectra features defined 26 main classes. Since B-DM was developed, several enhancements have been added. It has also become apparent that visual inspection of data is often necessary for correct classification, even though this introduces subjective judgments.

Meteorite classification depends on mineralogy, petrology, chemistry, and O-isotopes. Because meteorites can give higher resolution data and are easier to study, the classification process for meteorites is more robust than that of asteroids. We are using 2401 spectra of the well-defined meteorites as a starting point for a new asteroid taxonomy, leveraging >700 asteroid spectra from various sources. All meteorite and asteroid spectra were resampled to cover the range from 0.3 to 2.5 µm at 0.5 nm resolution and relabeled with a common set of headers for ease of analysis. Spectra were classified using logistic regression and k-nearest neighbors, two common machine learning algorithms. Results show that most meteorite classes have distinct spectra that are readily recognized by classifiers. For example, Nakhlites can be classified with 100% accuracy and CI meteorites can be classified with 89% accuracy. Because these groups’ spectra are so distinctive, we can match any individual asteroid spectrum against meteorite/asteroid data quantitatively. We are also experimenting with classifying the entire database (meteorites and asteroids) together to assess similarities among objects and groups of objects. The end result will be to develop a modern classification algorithm that extends well-characterized meteorite classes to a new asteroid taxonomy.