🧬 Revolutionary LLM-guided framework for automated algorithmic discovery in gravitational wave detection. Combines evolutionary optimization with Monte Carlo Tree Search to achieve 20.2% improvement over domain-specific methods and 59.1% over LLM-based optimization frameworks on MLGWSC-1 benchmark.
🌊 AI-powered gravitational wave detection system using Model Context Protocol (MCP) for efficient parameter space exploration. Features optimization client, analysis server, and automated parameter exploration for LIGO GW150914 data with LLM-agent validation.
The direct detection of gravitational waves by LIGO has confirmed general relativity (GR) and sparked rapid growth in gravitational wave (GW) astronomy. However, subtle post-Newtonian (PN) deviations observed during the analysis of high …
An interactive gravitational wave simulation ([LIGO-G2001983-v3](https://dcc.ligo.org/LIGO-G2001983)) in JavaScript created by [Graham Woan](https://dcc.ligo.org/cgi-bin/private/DocDB/ListBy?authorid=234), suitable for talks etc on spinning NSs and BBHs.
Python Script to plot cumulative GW Events until the end of O3a and Public Alerts in O3b
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.