Characteristics of asteroids and comets: implications of Tianwen-2 radar observations
Peer-Reviewed Publication
Updates every hour. Last Updated: 16-Aug-2025 02:11 ET (16-Aug-2025 06:11 GMT/UTC)
A recent review in journal Earth and Planetary Physics highlights that China's Tianwen-2 mission, launched on May 29, 2025, will carry a penetrating radar to directly probe the internal structures of the near-Earth asteroid 2016 HO₃ (Kamo'oalewa) and the active asteroid 311P/PANSTARRS. This investigation is expected to provide crucial data for unveiling the internal characteristics of asteroids and comets, thereby offering new insights into the early evolution of the solar system.
This is the first confirmed case of a star that survived an encounter with a supermassive black hole and came back for more. This discovery upends conventional wisdom about such tidal disruption events and suggests that these spectacular flares may be just the opening act in a longer, more complex story.
In a paper published in Earth and Planetary Physics, researchers propose a semi-empirical model combining Burton's empirical Dst formula with global magnetohydrodynamic (MHD) simulations to predict geomagnetic storm intensity. The hybrid approach demonstrates higher accuracy than pure empirical models when tested against moderate-to-intense storm events, while maintaining computational efficiency for operational space weather forecasting. This advancement enables more reliable Dst index estimation within global magnetosphere simulations.
New University of South Australia research is providing evidence of biological triggers of oil production in oats, a discovery that will help processing and potentially drive further demand for Australian-grown oats.
Researchers from the University of Tokyo in collaboration with Aisin Corporation have demonstrated that universal scaling laws, which describe how the properties of a system change with size and scale, apply to deep neural networks that exhibit absorbing phase transition behavior, a phenomenon typically observed in physical systems. The discovery not only provides a framework describing deep neural networks but also helps predict their trainability or generalizability. The findings were published in the journal Physical Review Research.