Let’s check Icarus for 1 January 2026 (vol. 443):
Souza-Feliciano, A. C. Rommel, F. L. Morgado, B. E. et al. Rotational Light Curve, phase, and visible colors of (52246) Donaldjohanson throughout Lucy Mission Flyby Art 116771
Hirabayashi, M. Prediction of Apophis’s deformation-driven rotational evolution during its closest encounter to the Earth in 2029 Art 116754
Nesvorny, D. Vokrouhlicky, D. Broz, M. et al. Discovery of 63 new young asteroid families Art 116768
Wu, F. Yang, J. Qu, G. et al. A deep learning-based two-stage feature matching method for small celestial body 3D shape reconstruction Art 116758
We can study asteroids from the ground, despite being ‘just’ points of light in the sky. Asteroids show a brightening-dimming pattern as they turn, side-on, then end-on, and back. With data in hand from the Lucy flyby, we can now check asteroid properties in flight (‘ground truth’ now ironically named) versus ground-telescope inferences.
Apophis will turn, the other way around- as it flies by Earth (safely) in 2029, our gravity should stretch it, nontrivially- ‘tidal force’. Depending on its prior rotation state, and the shape to be stretched, the new rotation state may be predictable. Hirabayashi take their opportunity at predicting that tidal process. Perhaps RAMSES will verify it!
Asteroid families are the fragments that emerge, shotgun-like, when a significant asteroid is shattered by impact. It’s a matter of diligence and math to deduce families, from the general asteroid background. Nesvorny et al. do their diligence.
Echoing Souza-Feliciano et al., Wu et al. try their turn, except using ML (Machine Learning). Unlike generative artificial intelligence, ML is (usually) bound problems, with explainable processes. Can it check out versus ground truths?