The Limited Predictability of Asteroid Spin Obliquity from Age, Size, Type, and Family: A Gaussian Process Regression Study

Abstract: Understanding the evolution of asteroid spin obliquity is crucial for studying the Yarkovsky–O'Keefe–Radzievskii–Paddack (YORP) effect and the influence of collisions. Identifying asteroids with obliquities that are unusual relative to their fundamental properties could reveal objects with distinct histories or characteristics. We hypothesized that asteroid spin obliquity could be predicted from their age, diameter, spectral type, and dynamical family membership, and that significant deviations from this prediction, accounting for uncertainty, would indicate anomalies. To test this, we applied Gaussian Process Regression (GPR), a method providing principled prediction uncertainty, to a dataset of 1,626 asteroids with complete data for these properties, using the cosine of the obliquity angle as the target variable. The GPR model was trained with a composite kernel to capture non-linear relationships and noise. Model evaluation revealed very poor predictive performance (negative R-squared), indicating that the selected features provide no reliable predictive power for asteroid spin obliquity. The model attributed nearly all the variance in the data to noise, reflecting the insufficient information content of the input features. Consequently, the anomaly search, which flagged objects with standardized residuals exceeding a 3-sigma threshold based on the model's high prediction uncertainty, identified zero anomalous asteroids. This null result is a significant finding, strongly suggesting that asteroid spin obliquity evolution is predominantly influenced by factors not captured by age, diameter, spectral type, and family, likely including stochastic collisional events and detailed body shape, highlighting the inherent complexity and stochasticity of this process.

Subjects:	astro-ph.EP; physics.space-ph
Cite as:	PX:2508.00055