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gw150914 -signal-search

🌊 GW150914 MCP Signal Search: AI-powered gravitational wave detection using Model Context Protocol (MCP). Features optimization client, analysis server, and automated parameter exploration for LIGO data.

Installation
Run this command in your terminal to add the MCP server to Claude Code.
Run in terminal:
Command
claude mcp add --transport stdio iphysresearch-gw150914-mcp-signal-search python mcp-server/gw_analysis_server.py \
  --env LOG_DIR="data/logs/" \
  --env DATA_PATH="data/" \
  --env OPENAI_API_KEY="your-openai-api-key-here"

How to use

This MCP server pair provides gravitational wave analysis tools and an AI-assisted optimization client for GW150914 signal search. The GW Analysis Server exposes MCP endpoints for data fetching, matched-filter searches, multi-detector network analysis, and visualization of results. The Optimization Client uses an AI agent to steer the search over a 4D parameter space (mass1, mass2, right ascension, declination) by learning from historical results and current SNR feedback. Run the server to start analysis services, then launch the client to begin AI-guided parameter exploration. The system stores execution logs and results under data/logs and can visualize analysis outputs via the server’s plotting utilities.

How to install

Prerequisites:

  • Python 3.9 or higher
  • UV package manager (uv)

Installation steps:

  1. Install UV (if not already installed):
curl -LsSf https://astral.sh/uv/install.sh | sh
  1. Clone the repository and set up the project:
git clone <repository-url>
cd gw150914-mcp-signal-search
make setup
  1. Configure environment variables:
cp env.template .env
# Edit .env to set your OPENAI_API_KEY and any other needed vars
  1. Install dependencies (development):
make install-dev
  1. Run the demo or start components as needed:
# Start a server and client
make run-client SERVER_PATH=mcp-server/gw_analysis_server.py

# Or run the full demo
make demo

Additional notes

Notes and tips:

  • Environment variables: set OPENAI_API_KEY for the optimization client. The server may also reference DATA_PATH and LOG_DIR to locate data and store logs.
  • Data handling: strain data and results live under data/; matched_filter_records.jsonl contains optimization history.
  • Logs: execution logs are saved under data/logs with timestamps for debugging.
  • If you encounter port or path issues, ensure the working directory is the repository root and that the Python path includes the mcp-server directory.
  • For production, consider separating the server and client into distinct processes or containers and redirect logs to a centralized location.

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