This tutorial provides a first introduction on how to use the DINGO package for analyzing gravitational wave data using neural posterior estimation. It illustrates at a 2D toy example how to train a DINGO model from scratch in a simplified setting. Furthermore, the tutorial shows how to download and use an already trained model to obtain posterior samples for GW150914. With this tutorial, We hope to help people get started with DINGO easily.

To get started quickly, run the tutorial in Google Colab by clicking the button above.

To run it locally (which may be faster), ensure that you create and activate a Python environment with the dingo-gw package.

With pip, this can be done with

python3 -m venv dingo-venv
source dingo-venv/bin/activate
pip install dingo-gw

If using conda, this can be done with

conda create -c conda-forge -n venv-dingo dingo-gw jupyterlab
conda activate venv-dingo

If you are looking for a more general introduction to posterior estimation of gravitational wave data without DINGO, please check out the tutorial "GW Parameter Inference with Machine Learning".