Gw150914 MCP Signal Search

🚀 GW150914 MCP Signal Search

This project offers a gravitational wave signal detection and optimization system. It leverages the Model Context Protocol (MCP) to efficiently explore the parameter space, aiming to analyze gravitational wave signals, especially the GW150914 event.

🚀 Quick Start

Prerequisites

• Python 3.9 or higher
• UV package manager

If you haven't installed UV yet, run the following command:

curl -LsSf https://astral.sh/uv/install.sh | sh

Installation

1. Clone and set up the project:

git clone <repository-url>
cd gw150914-mcp-signal-search
make setup

2. Configure environment variables:

cp env.template .env
# Edit .env with your OpenAI API key

3. Install dependencies:

make install-dev

Running the System

1. Start the analysis server and the optimization client:

make run-client SERVER_PATH=mcp-server/gw_analysis_server.py

2. Or run a complete demo (includes both server and client):

make demo

✨ Features

GW Analysis Server Tools

• Data Fetching: Download and preprocess GW data from GWOSC.
• Matched Filter Search: Perform template-based signal detection.
• Network Analysis: Conduct multi-detector coherent analysis.
• Visualization: Generate plots and analysis results.

Optimization Client

• AI-Powered Search: Utilize OpenAI GPT for intelligent parameter exploration.
• 4D Parameter Space: Optimize mass1, mass2, right ascension, and declination.
• Convergence Detection: Automatically stop when the optimal SNR is found.
• Progress Tracking: Provide visual feedback during optimization.

📦 Installation

Available Commands

Run make help to view all available commands:

Installation & Setup

• make install - Install production dependencies.
• make install-dev - Install with development dependencies.
• make setup - Complete development setup.
• make update - Update all dependencies.

Code Quality

• make format - Format code with black and isort.
• make lint - Run linting checks.
• make check - Run all code quality checks.
• make test - Run tests.

Running Applications

• make run-server - Start the GW analysis server.
• make run-client SERVER_PATH=<path> - Start the optimization client.
• make demo - Run a complete demo.

Development

• make notebook - Start Jupyter notebook.
• make dev-shell - Enter the development shell.
• make clean - Clean temporary files.

💻 Usage Examples

Basic Usage

# The client will automatically optimize parameters for GW150914
python mcp-client/gw_optimization_client.py mcp-server/gw_analysis_server.py

Advanced Usage

Edit the optimization query in the client to specify different:

• Mass ranges (currently 10 - 80 solar masses).
• Sky location ranges (RA: 0 - 2π, Dec: -π/2 to π/2).
• Convergence criteria.

📚 Documentation

🌊 Overview

This project offers tools for analyzing gravitational wave signals, with a specific focus on the GW150914 event. It employs MCP to establish a client - server architecture:

• GW Analysis Server (mcp-server/gw_analysis_server.py): Offers gravitational wave analysis tools via MCP.
• GW Optimization Client (mcp-client/gw_optimization_client.py): Uses AI to optimize signal detection parameters.

📊 Data Directory

The data/ directory contains gravitational wave strain data and analysis results for the GW150914 event:

Strain Data Files

• H1 - 1126259446 - 1126259478.txt: LIGO Hanford (H1) detector strain data.
  • GPS time range: 1126259446 to 1126259478 (32 seconds around GW150914).
  • Format: Two columns - GPS time (seconds) and strain amplitude.
  • Sample rate: 4096 Hz.
  • Data points: ~131,072 samples per detector.
• L1 - 1126259446 - 1126259478.txt: LIGO Livingston (L1) detector strain data.
  • Same format and time range as H1 data.
  • Contains the gravitational wave signal from the binary black hole merger.

Analysis Results

• matched_filter_records.jsonl: JSON Lines file containing optimization results.
  • Each line represents one matched filter analysis result.
  • Contains SNR values, template parameters, detector responses.
  • Tracks optimization history and parameter exploration.
  • Used by the AI client to learn from previous attempts.

Application Logs

• logs/: Directory containing timestamped execution logs (auto - generated).
  • server_YYYYMMDD_HHMMSS.log: Server execution logs with timestamps.
  • client_YYYYMMDD_HHMMSS.log: Client optimization logs and AI interactions.
  • demo_YYYYMMDD_HHMMSS.log: Combined logs from demo runs.
  • All stdout and stderr output is captured with timestamps.
  • Logs are automatically created when running make run-server, make run-client, or make demo.

Data Format Details

Strain Data Format:

GPS_TIME    STRAIN_AMPLITUDE
1.126259446000000000e+09    2.177040281448404468e-19
1.126259446000244141e+09    2.087638998822567751e-19
...

Analysis Results Format:

{
  "timestamp": "010725",
  "max_network_snr": 7.3055496414353565,
  "max_snr_time": 1126259462.4360352,
  "template_parameters": {
    "mass1": 17.0,
    "mass2": 9.5,
    "ra": 0.94,
    "dec": -0.31
  },
  "detector_snrs_at_max": {
    "H1": {"snr_abs": 5.064289109514169},
    "L1": {"snr_abs": 5.2653614670535624}
  }
}

Data Usage

• The strain data is automatically loaded by the MCP server for analysis.
• Results are cached to /tmp/gw - mcp/ during processing.
• The optimization client uses historical results to guide parameter searches.
• Data can be visualized using the server's plotting tools.
• All execution logs are automatically saved to data/logs/ with timestamps for debugging and analysis.

🔬 Scientific Background

This project focuses on the GW150914 event, the first direct detection of gravitational waves:

• Event Time: GPS 1126259462.427
• Source: Binary black hole merger (~36 + 29 solar masses)
• Detectors: LIGO Hanford (H1) and LIGO Livingston (L1)
• Peak SNR: ~24 in the network

📈 Results & Validation

This project was developed and validated at the AI for Science Hackathon (Beijing) with promising results demonstrating the effectiveness of LLM - agent powered gravitational wave detection.

Key Findings

Project presentation at AI for Science Hackathon (Beijing) - LLM - agent powered gravitational wave detection and scientific discovery

Reliability Analysis

The system demonstrates consistent convergence behavior across different initial conditions: Parameter evolution and convergence analysis showing gradual convergence to true values during exploration. The system shows consistent performance under different initial conditions with average iterations of 13.3 ± 3.5 to reach maximum SNR of 19.56.

Detection Performance

The automated system successfully detects and analyzes gravitational wave signals: Network SNR time series showing successful detection of GW150914 signal. The system achieves peak network SNR of 19.6 at GPS time 1126259462.428, demonstrating automated GW data detection with LLM - agent validation on real GW data.

Validation Results

• Convergence: Parameters gradually converge to true values during exploration.
• Consistency: The model shows consistent performance under different initial conditions.
• Accuracy: Successfully achieves a maximum SNR of ~19.6, matching theoretical expectations.
• Automation: The fully automated detection pipeline is validated on real LIGO data.

Technical Report

For detailed methodology, results, and analysis, see:

Presented by He Wang (ICTP - AP, UCAS) and Yiming Dong (PKU) at AI for Science Hackathon (Beijing), August 24, 2025

🔧 Technical Details

Development

Setting up Development Environment

# Complete development setup
make setup

# Install pre - commit hooks
make pre-commit-install

# Run tests
make test

# Check code quality
make check

Dependencies

Core dependencies:

• mcp: Model Context Protocol framework
• numpy, scipy: Scientific computing
• gwpy, pycbc: Gravitational wave analysis
• openai: AI integration
• matplotlib: Visualization

📄 License

This project is under the MIT License. See the LICENSE file for details.

📚 Learning Resources

MCP (Model Context Protocol) Resources

• Official MCP Documentation - Complete getting started guide and API reference.
• DeepLearning.AI MCP Course - "MCP: Build Rich - Context AI Apps with Anthropic" - comprehensive hands - on course by Elie Schoppik.
• [Tutorial on Building AI Scientist Agents with Model Context Protocol](https://drive.google.com/file/d/1rg0ZZjU - OgPZ4ZGvfYJPWgapbLeqKz5j/view?pli = 1) - Comprehensive tutorial on building AI scientist agents using the MCP framework.
• MCP.Science Repository - Open source MCP servers for scientific research with 65+ stars.
• How to Build Your Own MCP Server - Step - by - step tutorial for creating custom MCP servers.
• Integrate MCP Server Guide - Complete integration guide for MCP servers with AI applications.

Scientific Computing & AI Resources

• AI4Science Events - Stanford Quantum Science Hackathon and other AI for science events.
• AI - 4 - Science Organization - Community and resources for AI applications in scientific research.
• Project Technical Report - Detailed methodology and results from AI for Science Hackathon (Beijing) validation.

Why These Resources Matter

This project demonstrates practical applications of MCP in scientific computing, specifically for gravitational wave analysis. The resources above provide:

• Foundation Knowledge: Understanding MCP architecture and implementation.
• Hands - on Learning: Building and deploying MCP servers and clients.
• Scientific Context: Real - world applications of AI in physics and astronomy.
• Community: Connect with researchers and developers working on similar projects.

Getting Started with MCP

1. Start with the official MCP documentation for core concepts.
2. Take the DeepLearning.AI course for practical implementation.
3. Explore the MCP.Science repository for scientific use cases.
4. Use this project as a template for your own gravitational wave or scientific analysis applications.

🙏 Acknowledgments

• LIGO Scientific Collaboration for gravitational wave data.
• PyCBC and GWpy communities for analysis tools.
• Anthropic for MCP framework and protocol development.
• Path Integral Institute for MCP.Science open source servers.
• DeepLearning.AI and Elie Schoppik for educational resources.