A deep-learning system that can sift gravitational wave signals from background noise has been created by physicists in the UK. Deep learning is a neural-inspired pattern recognition technique that has already been applied to image processing, speech recognition and medical diagnoses, among other things.

Chris Messenger and colleagues at the University of Glasgow have shown that their system is as effective as conventional signal processing and has the potential to identify gravitational-wave signals much more quickly. Gravitational waves are ripples in space-time that can be observed using the LIGO-Virgo detectors – which are laser interferometers with pairs of arms several kilometres long positioned at right angles to each other.

As a wave passes through the Earth it very slightly stretches one arm while squeezing the other, before squeezing the first and stretching the second, and so on. This generates a series of tiny but distinctive oscillations that are recorded as variations in the interference patterns measured by the instruments.

The first gravitational wave to be detected was snared by the two LIGO detectors in the US in September 2015. Unlike signals observed since then, these oscillations were visible to the naked eye within the raw data.

Normally gravitational-wave signals are swamped by noise – seismic, thermal motion or photon statistics – that must be filtered out using computer algorithms if the signal is to emerge. Usually signals are picked out from the noise using a technique known as matched filtering. Read more from physicsworld.com…

thumbnail courtesy of physicsworld.com