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2021

From One to Many: A Deep Learning Coincident Gravitational-Wave Search

Schäfer, Marlin B., Nitz, Alexander H.

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Gravitational waves from the coalescence of compact-binary sources are now routinely observed by Earth bound detectors.

The most sensitive search algorithms convolve many different pre-calculated gravitational waveforms with the detector data and look for coincident matches between different detectors.
Machine learning is being explored as an alternative approach to building a search algorithm that has the prospect to reduce computational costs and target more complex signals.
In this work we construct a two-detector search for gravitational waves from binary black hole mergers using neural networks trained on non-spinning binary black hole data from a single detector.

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