Dingo (Deep Inference for Gravitational-wave Observations) is a Python program for analyzing gravitational wave data using neural posterior estimation. It dramatically speeds up inference of astrophysical source parameters from data measured at gravitational-wave observatories. Dingo aims to enable the routine use of the most advanced theoretical models in analyzing data, to make rapid predictions for multi-messenger counterparts, and to do so in the context of sensitive detectors with high event rates.

The basic approach of Dingo is to train a neural network to represent the Bayesian posterior conditioned on data. This enables amortized inference: when new data are observed, they can be plugged in and results obtained in a small amount of time. Tasks handled by Dingo include

• building training datasets;
• training normalizing flows to estimate the posterior density;
• performing inference on real or simulated data; and
• verifying and correcting model results using importance sampling.

The easiest way to install Dingo is using pip. Within a suitable virtual environment, run the command

pip install dingo-gw

This will install Dingo as well as all of its requirements, listed in pyproject.toml.

If you would like to make changes to Dingo, or to contribute to its development, you should install Dingo from source. To do so, first clone this repository:

git clone [email protected]:dingo-gw/dingo.git

Next create a virtual environment for Dingo, e.g.,

python3 -m venv dingo-venv
source dingo-venv/bin/activate

This creates and activates a venv for Dingo called dingo-venv. In this virtual environment, install Dingo:

cd dingo
pip install -e ."[dev]"

This command installs an editable version of Dingo, meaning that any changes to the Dingo source are reflected immediately in the installation. The inclusion of dev installs extra packages needed for development (code formatting, compiling documentation, etc.)

For instructions on using Dingo, please refer to the documentation.

Dingo is based on the following series of papers:

1. https://arxiv.org/abs/2002.07656: 5D toy model
2. https://arxiv.org/abs/2008.03312: 15D binary black hole inference
3. https://arxiv.org/abs/2106.12594: Amortized inference and group-equivariant neural posterior estimation
4. https://arxiv.org/abs/2111.13139: Group-equivariant neural posterior estimation
5. https://arxiv.org/abs/2210.05686: Importance sampling
6. https://arxiv.org/abs/2211.08801: Noise forecasting

If you use Dingo in your work, we ask that you please cite at least https://arxiv.org/abs/2106.12594.

Contributors to the code are listed in AUTHORS.md. We thank Vivien Raymond and Rory Smith for acting as LIGO-Virgo-KAGRA (LVK) review chairs. Dingo makes use of many LVK software tools, including Bilby, bilby_pipe, and LALSimulation, as well as third party tools such as PyTorch and nflows.

For questions or comments please contact Maximilian Dax or Stephen Green.