This pull request enables support for 1 detector networks.
There are two current issues with one detector flows for Dingo.
1). https://github.com/dingo-gw/dingo/issues/216. Summarized, there is an issue when using a coupling layer with a 1D distribution with no context as there is no input to the neural network. This causes nflows to error out.
This PR fixes this by using an autoregressive flow to learn the 1D density of GNPE samples.
2). Currently, there is no way to pass no context during gibbs sampling
There is a simple fix to this which is in lines 503 of dingo.core.samplers.py.
This PR fixes the issues. It has been tested on GW190620 which is a L1 only event. It achieves a sample efficiency of 6.57% with 32_000 effective samples in this case.
This pull request enables support for 1 detector networks.
There are two current issues with one detector flows for Dingo.
1). https://github.com/dingo-gw/dingo/issues/216. Summarized, there is an issue when using a coupling layer with a 1D distribution with no context as there is no input to the neural network. This causes nflows to error out.
2). Currently, there is no way to pass no context during gibbs sampling
This PR fixes the issues. It has been tested on GW190620 which is a L1 only event. It achieves a sample efficiency of 6.57% with 32_000 effective samples in this case.