Dingo (Deep Inference for Gravitational-wave Observations)
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.
Author(s): | Maximilian Dax, Stefen Green, Michael Pürrer, Nihar Gupte, Jonas Wildberger |
Department(s): |
Empirical Inference |
Research Projects(s): |
Astronomy |
Authors: | Maximilian Dax, Stefen Green, Michael Pürrer, Nihar Gupte, Jonas Wildberger |
License: | The MIT License (MIT) |
Repository: | https://github.com/dingo-gw/dingo |
Documentation: | https://dingo-gw.readthedocs.io |