Tunable Kernel-Nulling for Direct Exoplanet Detection

📜 Abstract

This project focuses on the development and optimization of a tunable Kernel-Nulling interferometer for direct detection of exoplanets. The work combines numerical simulations, calibration algorithms, and statistical analysis techniques to achieve high-contrast detection capabilities using a four-telescope architecture with integrated photonic components.

🎯 Objectives

• Direct exoplanet detection with contrasts beyond 10⁻⁸
• Phase aberration correction using active photonic components with 14 electro-optic phase shifters
• Performance optimization through advanced calibration algorithms
• Statistical analysis of kernel-null depth distributions

🚀 Getting Started

Prerequisites

• Python 3.11 or higher
• PDM (Python Dependency Manager)

Key Dependencies

• numpy - Numerical computations
• astropy - Astronomical units and calculations
• matplotlib - Plotting and visualization
• scipy - Scientific computing
• numba - High-performance numerical functions
• ipywidgets - Interactive widgets for Jupyter notebooks

Installation

1. Clone the repository:

git clone https://github.com/your-username/Tunable-Kernel-Nulling.git
cd Tunable-Kernel-Nulling

2. Install project dependencies (using PDM):

pdm install

Thes open the main simulation notebook "numerical_simulation.ipynb" and select the appropriate kernel for your environment.

🔬 Scientific Approach

Architecture

The system employs a four-telescope Kernel-Nulling architecture using integrated optical components:

• 4 Telescopes: Collecting light from target star and potential companions
• 14 Phase Shifters: Electro-optic elements for phase correction
• MMI Components: Multi-mode interferometers for signal processing
• 7 Outputs: 1 bright output + 6 dark outputs → 3 kernel outputs

Key Features

1. Calibration Algorithms:

   • Genetic algorithm approach
   • Input obstruction method
   • Performance comparison and optimization

2. Statistical Analysis:

   • ROC curve analysis
   • P-value computation
   • Multiple test statistics (mean, median, Kolmogorov-Smirnov, etc.)

3. Simulation Scenarios:

   • VLTI: Ground-based, 8m telescopes, 130m baseline, λ=1.55μm
   • LIFE: Space-based, 2m telescopes, 600m baseline, λ=4μm

Applications

• Direct imaging of exoplanets
• High-contrast astronomy
• Interferometric nulling techniques
• Statistical detection methods

📊 Key Results

• Achievable contrasts: 10⁻⁵ to 10⁻⁶ (limited by phase perturbations)
• Robust performance against first-order phase aberrations
• Statistical tests demonstrate reliable planet detection capabilities
• Successful calibration algorithms for component optimization

📚 Publications

This work has contributed to several scientific publications:

1. SPIE Proceedings - "Tunable Kernel-Nulling interferometry for direct exoplanet detection"
2. A&A Paper (in preparation) - "Tunable Kernel-Nulling for direct detection of exoplanets: 1. Calibration and performance"
3. Statistical Analysis Paper (in preparation) - "Statistical data analysis techniques for kernel-nulling interferometry"

👥 Contributors

• Vincent Foriel - PhD Student, Primary Developer
• David Mary - Supervisor, Statistical Analysis
• Frantz Martinache - Supervisor, Interferometry Expert
• Nick Cvetojevic - Photonics Specialist
• Romain Laugier - Kernel-Nulling Theory
• Marc-Antoine Martinod - Technical Support
• Sylvie Robbe-Dubois - Project Coordination
• Roxanne Ligi - Scientific Advisor

🏢 Affiliations

• Université Côte d'Azur, Observatoire de la Côte d'Azur Nice
• CNRS, Laboratoire Lagrange, Nice, France
• KU Leuven University, Leuven, Belgium

📞 Contact

For questions or collaborations:

• Vincent Foriel: [email protected]
• Frantz Martinache: [email protected]
• David Mary: [email protected]