AI-Driven Breakthrough Enhances LIGO’s Gravitational Wave Detection by Reducing Noise

Researchers have developed an AI-powered tool that autonomously detects environmental patterns affecting LIGO’s sensitivity, paving the way for more precise gravitational wave discoveries and deeper insights into cosmic phenomena.

Image Credit: University of California, RiversideImage Credit: University of California, Riverside

*Important notice: arXiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as definitive, used to guide development decisions, or treated as established information in the field of artificial intelligence research.

Thanks to the work of scientists at the University of California, Riverside, finding patterns and reducing noise in large, complex datasets generated by the gravitational wave-detecting LIGO facility just got easier. 

The UCR researchers presented a paper at a recent IEEE big-data workshop, demonstrating a new, unsupervised machine-learning approach to finding 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 comprising the output of environmental sensors 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 external disturbance. Ground motion and any vibrational motion—from the wind to ocean waves striking the coast of Greenland or the Pacific—can affect the experiment's sensitivity and data quality, resulting in "glitches" or periods of increased noise bursts. 

"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."

Vagelis Papalexakis, an associate professor of computer science and engineering who holds the Ross Family Chair in Computer Science, presented the team's paper, titled "Multivariate Time Series Clustering for Environmental State Characterization of Ground-Based Gravitational-Wave Detectors," at the IEEE's 5th International Workshop on Big Data & AI Tools, Models, and Use Cases for Innovative Scientific Discovery that took place last month in Washington, D.C.

"The way our machine learning approach works is that we take a model tasked with identifying patterns in a dataset and we let the model find patterns on its own," Papalexakis said. "The tool was able to identify the same patterns that very closely correspond to the physically meaningful environmental states that are already known to human operators and commissioners at the LIGO sites."

Papalexakis added that the team had worked with the LIGO Scientific Collaboration to secure the release of a very large dataset related to the analysis reported in the research paper. This data release allows the research community to validate the team's results and develop new algorithms to identify patterns in the data.

"We have identified a fascinating link between external environmental noise and the presence of certain types of glitches that corrupt the quality of the data," Papalexakis said. "This discovery has the potential to help eliminate or prevent the occurrence of such noise."

The team organized and worked through all the LIGO channels for about a year. Richardson noted that the data release was a major undertaking. 

"Our team spearheaded this release on behalf of the whole LIGO Scientific Collaboration, which has about 3,200 members," he said. "This is the first of these particular types of datasets and we think it's going to have a large impact in the machine learning and the computer science community."

Richardson explained that the tool the team developed can take information from signals from numerous heterogeneous sensors that measure different disturbances around the LIGO sites. The tool can distill the information into a single state, he said, that can then be used to search for time series associations of when noise problems occurred in the LIGO detectors and correlate them with the sites' environmental states at those times.

"If you can identify the patterns, you can make physical changes to the detector - replace components, for example," he said. "The hope is that our tool can shed light on physical noise coupling pathways that allow for actionable experimental changes to be made to the LIGO detectors. Our long-term goal is for this tool to be used to detect new associations and new forms of environmental states associated with unknown noise problems in the interferometers."

Pooyan Goodarzi, a doctoral student working with Richardson and a co-author on the paper, emphasized the importance of publicly releasing the dataset. 

"Typically, such data tend to be proprietary," he said. "We managed, nonetheless, to release a large-scale dataset that we hope results in more interdisciplinary research in data science and machine learning."

The team's research was supported by a grant from the National Science Foundation, awarded through a special program called Advancing Discovery with AI-powered tools. This program focused on applying artificial intelligence/machine learning to address problems in the physical sciences. 

Richardson, Papalexakis, and Goodarzi were joined in the research by Rutuja Gurav, a doctoral student working with Papalexakis; Isaac Kelly, a summer undergraduate REU student; Anamaria Effler of the LIGO Livingston Observatory; and Barry Barish, a distinguished professor of physics and astronomy at UCR.

*Important notice: arXiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as definitive, used to guide development decisions, or treated as established information in the field of artificial intelligence research.

Source:
Journal reference:
  • Preliminary scientific report. Gurav, R., Kelly, I., Goodarzi, P., Effler, A., Barish, B., Papalexakis, E., & Richardson, J. (2024). Multivariate Time Series Clustering for Environmental State Characterization of Ground-Based Gravitational-Wave Detectors. ArXiv. https://arxiv.org/abs/2412.09832

Comments

The opinions expressed here are the views of the writer and do not necessarily reflect the views and opinions of AZoAi.
Post a new comment
Post

While we only use edited and approved content for Azthena answers, it may on occasions provide incorrect responses. Please confirm any data provided with the related suppliers or authors. We do not provide medical advice, if you search for medical information you must always consult a medical professional before acting on any information provided.

Your questions, but not your email details will be shared with OpenAI and retained for 30 days in accordance with their privacy principles.

Please do not ask questions that use sensitive or confidential information.

Read the full Terms & Conditions.