Orateur
Description
The LISA space-interferometer will simultaneously acquire gravitational waves emitted from thousands of sources through three time series.
The disentanglement of these signals poses a challenging underdetermined source separation problem.
To isolate signals based on their individual signatures, we introduce a new source separation algorithm based on learning signal representations in a similar fashion to autoencoders.
Our method makes it possible to efficiently extract physically meaningful signals from the entangled data.
This will be illustrated on realistic simulations of the LISA data.