Emergent Sensitivity: The Planetary Interferometer

Overview

This repository contains the simulation code, numerical models, and data generation scripts for the paper:

Emergent Sensitivity: A Systems-Theoretic Framework for the Optimization of the Global Gravitational Wave Network Mahomane R. M. Ronald & Govender Megandhren Submitted to Classical and Quantum Gravity (IOP Publishing)

This project models the global network of gravitational wave detectors (LIGO, Virgo, KAGRA, LIGO-India) not as isolated instruments, but as a single, coherent "Planetary Interferometer." It demonstrates how system-level coordination specifically regarding duty cycles and glitch rejection can yield scientific performance gains comparable to hardware upgrades.

Key Features

The Jupyter Notebook (.ipynb) included in this repository reproduces the following key results from the manuscript:

1. Network Antenna Patterns: Visualization of the "blind spots" in 2-detector networks vs. the global coverage of 3+ detector networks (reproducing Figure 1 and Figure 2).
2. Superlinear Scaling Laws: Numerical verification of Theorem A.3, demonstrating that sky localization area ($\Omega_{90}$) scales super-linearly with detector count due to phase triangulation, outperforming the standard $1/\sqrt{N}$ reductionist prediction (reproducing Figure 3).
3. Coordinated Schedule Simulation: A time-domain Markov Chain Monte Carlo (MCMC) simulation comparing "Stochastic" (independent) maintenance schedules against a "Coordinated" (system-theoretic) schedule. This validates the efficiency gains in the Triple-Lock probability $P_{k\geq3}$ (reproducing Figure 4 and Table 1).
4. Feedback Stability: Numerical validation of the control-theoretic stability condition ($|\alpha| < 1$) for the Global Glitch Rejection Feedback Loop derived in Appendix G.3.

Installation

To run these simulations locally, you need Python 3.8+ and the following scientific libraries.

1. Clone the repository:

   git clone https://github.com/Nube1/planetary-interferometer-optimization.git
   cd planetary-interferometer-optimization

2. Install dependencies:

   pip install numpy scipy matplotlib healpy pandas seaborn

   (Note: gwpy and pycbc are optional for the core geometric simulations but recommended for full signal processing reproduction.)

Usage

The primary simulation is contained in the Jupyter Notebook.

1. Launch Jupyter:

   jupyter notebook

2. Open Planetary_Interferometer_Simulation.ipynb.
3. Run all cells to generate the figures and statistical outputs.

Running on Google Colab

If you prefer not to install dependencies locally, you can upload the .ipynb file directly to Google Colab.

Project Structure