MACHINE LEARNING APPROACHES TO ASTEROID TAXONOMY USING METEORITE SPECTRA
For this study, we acquired roughly 350 meteorites and prepared them as powders for spectral acquisition at the Reflectance Experiment Laboratory (RELAB). We then merged this data set with existing data on meteorite powders, slabs, and chips from RELAB and the University of Winnipeg. All spectra were resampled to cover the range from 0.3 to 2.5 µm at 0.01 µm resolution; data are posted on nemo.mtholyoke.edu. ML models were trained on 1,422 RELAB spectra spanning 21 meteorite classes as given in the Meteoritical Bulletin Database. Because similarities in mineralogy make classes with different textures spectrally indistinguishable, we created nine distinct classes for training: CM/C2/CR, CO/CV, CK/R/brachinites, L/H/LL/ureilites, EH/EL, Acapulcoites/lodranites, IAB/IIAB, aubrites, and howardites/eucrites/diogenites. Cross-validation was used for hyperparameter tuning and model selection. We tested the accuracy of classification models with as-acquired data versus data normalized to the intensity at a single channel. Then we chose the best model by testing all channels in the wavelength range at 0.05 μm increments. Normalization is needed to improve model generalization performance when classifying spectra from varying laboratories and telescopes. ML models including Kernel Fisher Discriminant Analysis (KFDA), Kernel Support Vector Machine (SVM), Logistic Regression, and Quadratic Discriminant Analysis (QDA) achieved test classification accuracies ranging between 85-93% within the RELAB dataset. Models were tested on the meteorite data from the University of Winnipeg with comparable accuracies. The best model was then used to classify asteroids into the nine new classes to better understand their compositional and spatial diversity and their relationship to meteorites.