A new method of analysing the complex data from massive astronomical events could help gravitational wave astronomers avoid a looming computational crunch.

Researchers from the University of Glasgow have used machine learning to develop a new system for processing the data collected from detectors like the Laser Interferometer Gravitational-Wave Observatory (LIGO).

The system, which they call VItamin, is capable of fully analysing the data from a single signal collected by gravitational wave detectors in less than a second, a significant improvement on current analysis techniques.

Since the historic first detection of the ripples in spacetime caused by colliding black holes in 2015, gravitational wave astronomers have relied on an array of powerful computers to analyse detected signals using a process known as Bayesian inference.

A full analysis of each signal, which provides valuable information about the mass, spin, polarisation and inclination of orbit of the bodies involved in each event, can currently take days to be completed.

Since that first detection, gravitational wave detectors like LIGO in the USA and Virgo in Italy have been upgraded to become more sensitive to weaker signals, and other detectors like KAGRA in Japan have come online.

As a result, gravitational wave signals are being detected with increasing regularity, putting the current computing infrastructure under greater strain to analyse each detection. As detector performance continues to improve thanks to upgrades between each observing run, there is a risk that the capacity of the system to process a greater number of signals will be overwhelmed.

VItamin was developed by researchers from the University of Glasgow’s School of Physics & Astronomy in collaboration with colleagues from the School of Computing Science.

In a new paper published today in the journal Nature Physics, they describe how they ‘trained’ VItamin to recognise gravitational wave signals from binary black holes using a machine learning technique called a conditional variational autoencoder, or CVAE.

The team created a series of simulated gravitational wave signals, overlaid with noise to mimic the background noise from which gravitational wave detectors have to pick each detection. Then, they passed them through the machine learning system around 10 million times.

Over the course of the process, VItamin improved its ability to pick out signals and analyse 15 parameters until it was capable of providing accurate results in less than a second.

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