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 analysing 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.
As training a network from scratch can be expensive, we intend to also distribute trained networks that can be used directly for inference. These can be used with dingo_pipe to automate analysis of gravitational wave events.
.. toctree::
:caption: Getting started
:maxdepth: 1
installation
overview
quickstart
.. toctree::
:caption: Examples
:maxdepth: 1
example_toy_npe_model
example_npe_model
example_gnpe_model
example_injection
.. toctree::
:caption: Advanced guide
:maxdepth: 1
sbi
code_design
generating_waveforms
waveform_dataset
training_transforms
noise_dataset
network_architecture
training
inference
gnpe
result
dingo_pipe
.. toctree::
:caption: API
:maxdepth: 1
modules
Dingo is based on a series of papers developing neural posterior estimation for gravitational waves, starting from proof of concept {cite:p}Green:2020hst, to inclusion of all 15 parameters and analysis of real data {cite:p}Green:2020dnx, noise conditioning and full amortization {cite:p}Dax:2021tsq, and group-equivariant NPE {cite:p}Dax:2021myb. Dingo results are augmented with importance sampling in {cite:p}Dax:2022pxd. Finally, training with forecasted noise (needed for training prior to an observing run) is described in {cite:p}Wildberger:2022agw.
.. bibliography::
If you use Dingo in your work, we ask that you please cite at least {cite:p}Dax:2021tsq.
Contributors to the code are listed in AUTHORS.md. We thank Vivien Raymond and Rory Smith for acting as LIGO-Virgo-KAGRA (LVK) code reviewers. 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.
* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`