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Free keywords:
Astrophysics, Instrumentation and Methods for Astrophysics, astro-ph.IM,Computer Science, Learning, cs.LG,General Relativity and Quantum Cosmology, gr-qc
Abstract:
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. The
network is applied to the data from both observatories independently and we
check for events coincident in time between the two. This enables the efficient
analysis of large quantities of background data by time-shifting the
independent detector data. We find that while for a single detector the network
retains $91.5\%$ of the sensitivity matched filtering can achieve, this number
drops to $83.9\%$ for two observatories. To enable the network to check for
signal consistency in the detectors, we then construct a set of simple networks
that operate directly on data from both detectors. We find that none of these
simple two-detector networks are capable of improving the sensitivity over
applying networks individually to the data from the detectors and searching for
time coincidences.
