Unveiling the Intrinsic Structure of the Asteroid Belt: Correcting for Observational Selection Bias in Physical and Compositional Properties

Abstract: Asteroid studies face significant challenges due to data sparsity and observational biases, limiting our understanding of the asteroid belt's true composition and structure. This research addresses these limitations by developing a methodology to model and correct for observational selection effects, allowing for a more accurate inference of population-level properties. We leverage a comprehensive dataset of over 1.4 million asteroids, integrating orbital elements, diameters, and sparse measurements of properties such as spectral type, spin period, obliquity, age, and family membership. Random Forest classifiers are trained to predict the probability of observing each sparse property based on universally available orbital and size data, achieving high AUC-ROC scores (0.86-0.99) and strong calibration. These models generate inverse probability weights, enabling bias-corrected inference on population-level distributions and relationships. Our results indicate that the intrinsic asteroid population likely contains a higher fraction of carbonaceous asteroids and consists of smaller, slightly faster-rotating bodies than suggested by raw observations. Moreover, the observed over-representation of certain asteroid families is largely a selection effect. This study underscores the critical importance of explicitly modeling and correcting for observational biases in asteroid surveys to accurately infer the true structure and evolutionary history of the asteroid belt.

Subjects:	astro-ph.EP; physics.space-ph
Cite as:	PX:2508.00054