When massive galaxies act as cosmic magnifying glasses, they can bend and amplify the gravitational waves from distant black hole mergers - but predicting how often this happens requires sophisticated statistical modeling across cosmic distances. LeR tackles one of gravitational wave astronomy’s most computationally challenging problems: calculating the rates at which we should detect these lensed events.
This Python package provides a complete simulation pipeline for gravitational wave lensing statistics, from sampling source properties using realistic merger rate densities to modeling lens galaxy parameters with the Elliptical Power Law framework. It integrates seamlessly with established tools like Lenstronomy for ray-tracing calculations and bilby for parameter estimation, while leveraging multiprocessing to handle the computationally intensive image generation. The toolkit calculates everything from optical depths and magnifications to time delays and detection rates, giving researchers the statistical foundation needed for population studies.
With LIGO-Virgo-KAGRA observations rapidly accumulating and next-generation detectors on the horizon, LeR fills a critical gap for gravitational wave research groups studying population demographics and lensing signatures. The package’s modular design allows easy integration of custom merger rate models and lens galaxy distributions, making it invaluable for forecasting detection rates and optimizing search strategies for the era of precision gravitational wave astronomy.
⭐ Stars: 6
💻 Language: Python
🔗 Repository: hemantaph/ler