A framework for automated discovery of gravitational-wave detection algorithms, combining LLM guidance with Evolutionary Monte Carlo Tree Search, enabling efficient and creative pipeline discovery.
Comprehensive review of data analysis challenges in space-based gravitational wave detection (LISA, Taiji, TianQin) and transformative applications of artificial intelligence in addressing these challenges.
Deep learning method for rapid detection of massive black hole binary coalescences in LISA data, achieving high sensitivity with no false alarms while processing 1-year data in seconds.
Pioneer exploration of deep learning applications in gravitational wave data analysis, addressing challenges in signal detection, computational efficiency, and discovery of unexpected signals beyond theoretical templates.