Finding patterns and reducing noise in large, complex datasets generated by the gravitational wave-detecting LIGO facility just got easier, thanks to the work of scientists at the University of California, Riverside.
The UCR researchers presented a paper at a recent IEEE big-data workshop, demonstrating a new, unsupervised machine learning approach to find new patterns in the auxiliary channel data of the Laser Interferometer Gravitational-Wave Observatory, or LIGO. The technology is also potentially applicable to large scale particle accelerator experiments and large complex industrial systems.
LIGO is a facility that detects gravitational waves — transient disturbances in the fabric of spacetime itself, generated by the acceleration of massive bodies. It was the first to detect such waves from merging black holes, confirming a key part of Einstein’s Theory of Relativity. LIGO has two widely-separated 4-km-long interferometers — in Hanford, Washington, and Livingston, Louisiana — that work together to detect gravitational waves by employing high-power laser beams. The discoveries these detectors make offer a new way to observe the universe and address questions about the nature of black holes, cosmology, and the densest states of matter in the universe.
Each of the two LIGO detectors records thousands of data streams, or channels, which make up the output of environmental sensors located at the detector sites.
“The machine learning approach we developed in close collaboration with LIGO commissioners and stakeholders identifies patterns in data entirely on its own,” said Jonathan Richardson, an assistant professor of physics and astronomy who leads the UCR LIGO group. “We find that it recovers the environmental ‘states’ known to the operators at the LIGO detector sites extremely well, with no human input at all. This opens the door to a powerful new experimental tool we can use to help localize noise couplings and directly guide future improvements to the detectors.”
Richardson explained that the LIGO detectors are extremely sensitive to any type of external disturbance. Ground motion and any type of vibrational motion — from the wind to ocean waves striking the coast of Greenland or the Pacific — can affect the sensitivity of the experiment and the data quality, resulting in “glitches” or periods of increased noise bursts, he said.
“Monitoring the environmental conditions is continuously done at the sites,” he said. “LIGO has more than 100,000 auxiliary channels with seismometers and accelerometers sensing the environment where the interferometers are located. The tool we developed can identify different environmental states of interest, such as earthquakes, microseisms, and anthropogenic noise, across a number of carefully selected and curated sensing channels.”