dingo-gw 0.9.7

Deep inference for gravitational-wave observations

Dingo

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.

Installation

Pip

To install using pip, run the following within a suitable virtual environment:

pip install dingo-gw

This installs Dingo and its runtime dependencies, as specified in
pyproject.toml.

Optional functionality can be enabled via extras, for example:

pip install "dingo-gw[wandb,pyseobnr]"

Conda

Dingo is also available from the conda-forge repository.
To install using conda, first activate a conda environment, then run:

conda install -c conda-forge dingo-gw

Development install

If you would like to make changes to Dingo or contribute to its development, install it from source.

First clone the repository:

git clone [e-mail]:dingo-gw/dingo.git
cd dingo-gw

Recommended (using uv)

We recommend using uv for development installs, as it provides fast, reproducible dependency resolution.

Create a virtual environment and install all development dependencies:

uv sync

This installs Dingo in editable mode along with development, documentation, and typing dependencies. To also install optional dependencies, use

uv sync --extra wandb --extra pyseobnr

Alternative (pip)

If you prefer pip, create and activate a virtual environment:

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

Install Dingo in editable mode with development tools:

pip install -e ".[dev]"

Optional user-facing features can be enabled via extras, for example:

pip install -e ".[dev,wandb]"

Usage

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

References

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
7. https://arxiv.org/abs/2407.09602: Binary neutron star inference

Dingo was used also in https://arxiv.org/abs/2404.14286 to find evidence for eccentric binaries.

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 Charlie Hoy, 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.

Contact

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