Core Insights - The "Wukong" satellite has achieved the first precise measurement of the secondary cosmic ray boron spectrum in the TeV/n energy range, providing new observational evidence for revising cosmic ray propagation models [1][2] - The satellite's findings indicate a significant hardening structure in the boron spectrum around 200 GeV/n, suggesting that cosmic ray propagation may be slower than previously anticipated [2] Group 1: Scientific Achievements - The "Wukong" satellite, launched by China, is the first astronomical satellite dedicated to observing high-energy particles in space, with core scientific goals including dark matter particle detection and cosmic ray research [1] - The international collaboration group utilized eight years of observational data to achieve precise measurements of the boron element spectrum from 10 GeV/n to 8 TeV/n, surpassing previous space detection experiments in both measurement precision and energy limits [2] Group 2: Implications for Cosmic Ray Research - The observed hardening of the boron spectrum indicates that the particle flux at higher energies significantly exceeds classical model predictions, with the spectrum index increase being approximately twice that of primary cosmic ray protons and helium nuclei [2] - These findings are crucial for understanding the acceleration and propagation mechanisms of cosmic rays, as they provide insights into the diffusion process of cosmic rays in the universe [2]

Core Viewpoint - The exploration for extraterrestrial life is being revolutionized by the integration of artificial intelligence (AI) into astronomical research, enhancing data analysis and hypothesis generation capabilities [1][2][6]. Group 1: AI in Astrobiology - NASA's "AstroAgents" system, consisting of eight AI agents, is designed to autonomously conduct research in astrobiology, analyzing data and generating scientific hypotheses [2][3]. - The system aims to study Martian samples to identify organic molecules that may indicate past or present life [2][3]. - AI agents utilize large language models to actively participate in research, determining research content and methods, and evaluating results [2][3]. Group 2: Discovering Exoplanets - The ExoMiner project, developed by NASA scientists, has successfully identified 370 previously unknown exoplanets using machine learning techniques [4][5]. - Despite the discovery, none of the identified exoplanets have environments similar to Earth, indicating a challenging search for habitable conditions [4][5]. Group 3: SETI and Electromagnetic Signal Monitoring - The SETI program is employing AI to analyze electromagnetic signals across a wide wavelength range, enhancing the search for extraterrestrial intelligence [6][7]. - A new AI-driven software system is being developed for the Very Large Array (VLA) to process vast amounts of data, significantly improving the efficiency of signal detection [6][7]. - SETI's efforts include scanning millions of stars and hundreds of galaxies for signs of life, with AI also being used to analyze Martian rock samples for biological indicators [6][7].