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Addressing out-of-distribution data for flow-based gravitational wave inference
Maximilian Dax · Stephen Green · Jonas Wildberger · Jonathan Gair · Michael Puerrer · Jakob Macke · Alessandra Buonanno · Bernhard Schölkopf

Simulation-based inference and normalizing flows have recently demonstrated excellent performance when applied to gravitational-wave parameter estimation. These methods can provide accurate results within seconds, in cases where classical methods based on stochastic samplers may take days or even weeks. However, such methods are typically based on deep neural networks and thus unable to reliably deal with out-of-distribution data, such as may arise when predicted signal and noise models do not precisely fit observations. We here present two innovations to deal with this challenge. First, we introduce a probabilistic noise model to augment the training data, making the inference network substantially more robust to distribution shifts in experimental noise. Second, we apply importance sampling to independently verify and correct inference results. This compensates for network inaccuracies and flags failure cases via low sample efficiencies. We expect these methods to be key components for the integration of deep learning techniques into production pipelines for gravitational-wave analysis.

Author Information

Maximilian Dax (MPI for Intelligent Systems, Tübingen)
Stephen Green (Albert Einstein Institute Potsdam)
Jonas Wildberger (Max Planck Institute for Intelligent Systems, Tübingen)
Jonathan Gair (Max Planck Institute for Gravitational Physics)
Michael Puerrer (University of Rhode Island)
Jakob Macke (University of Tuebingen)
Alessandra Buonanno (Max Planck Institute for Gravitational Physics)
Bernhard Schölkopf (MPI for Intelligent Systems, Tübingen)

Bernhard Scholkopf received degrees in mathematics (London) and physics (Tubingen), and a doctorate in computer science from the Technical University Berlin. He has researched at AT&T Bell Labs, at GMD FIRST, Berlin, at the Australian National University, Canberra, and at Microsoft Research Cambridge (UK). In 2001, he was appointed scientific member of the Max Planck Society and director at the MPI for Biological Cybernetics; in 2010 he founded the Max Planck Institute for Intelligent Systems. For further information, see www.kyb.tuebingen.mpg.de/~bs.

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