First predicted by Einstein’s theory of General relativity over 100 years ago, gravitational wave detection by the Laser Interferometer Gravitational-Wave Observatory (LIGO) has improved greatly since its first detection in 2015. Now, joined by the Virgo and KAGRA observatories, in the middle of its 4th observing run, LIGO has recorded almost 100 confirmed signals, all of which are ‘modeled’ compact object coalescences (CBCs) from binary black holes and neutron stars. These types of signals are known models since these collisions follow a well-defined waveform template. This template is then used to find the incredibly small disturbance in spacetime captured by the interferometers as the gravitational wave passes through Earth.
Credit: Ingrid Bourgault, Illustration for the Announcement of new Results from the LIGO-Virgo-Kagra Collaboration
Gravitational Wave Anomalous Knowledge (GWAK) is a neural network-based technique designed for the detection of unmodeled gravitational wave signals. Unmodeled signals, like those from core-collapse supernovae, are different from modeled signals because they don’t have a well-defined waveform. Heavy reliance on template waveforms means these signals can’t be detected the traditional way, which uses a matched filtering technique. Building on the method described here, GWAK uses semi-supervised learning to look for unmodeled signals. By training autoencoders on different types of data like detector noise, glitches, and simulated CBC signals, GWAK creates a low-dimensional representation of data allowing the network to easily distinguish signals from noise.
Testing using the LIGO-Virgo-Kagra collaboration’s complete catalogue of data from its third observing run revealed that while GWAK didn’t identify any statistically significant unmodeled signals, it was able to maintain sensitivity in high-glitch periods where many searches avoid analysis due to intense noise. This robustness across a broad range of noise types, as well as detection of confirmed CBC signals, demonstrate GWAK’s potential to broaden the scope of gravitational wave detection. Its ability to analyze traditionally discarded data opens new possibilities for future discovery. Incorporation of heuristic models to down-weight glitch-like events and frequency-domain correlations to reduce false alarms demonstrate its adaptability in addressing real detection challenges.
Overall, this study lays the foundation for GWAK’s potential in upcoming observing runs, offering an exciting opportunity to explore the universe in a novel way.
arXiv: 2309.11537 By: Ryan Raikman1,5, Eric A. Moreno2, Ekaterina Govorkova2, Ethan J Marx1,2, Alec Gunny1,2, William Benoit3, Deep Chatterjee1,2, Rafia Omer3, Muhammed Saleem3, Dylan S Rankin4, Michael W Coughlin3, Philip C Harris2, Erik Katsavounidis1,2
April 19, 2024
Credit: Ingrid Bourgault, Illustration for the Announcement of new Results from the LIGO-Virgo-Kagra Collaboration
The Laser Interferometer Gravitational-Wave Observatory (LIGO) represents a pinnacle in the quest to detect gravitational waves, ripples in the fabric of spacetime predicted by Albert Einstein’s general theory of relativity. Gravitational wave detectors like LIGO are marvels of precision engineering designed to observe the most cataclysmic events in the universe, such as the collisions of black holes and neutron stars. Consisting of two 4-kilometer long interferometers in Hanford, Washington and Livingston, Louisiana, LIGO operates by measuring infinitesimally small disturbances in spacetime caused by passing gravitational-waves. Our goal is to increase the search capabilities of these detectors to never-before-seen phenomena like supernovae explosions, cosmic strings, and gravitational bremsstrahlung, just to name a few.
GWAK: New Methods of Anomaly Evaluation on the Horizon
The detection of gravitational wave (GW) signals is pivotal for unraveling the mysteries of the cosmos, relying heavily on accurately modeled templates of GW emissions. However, the possibility of unmodeled transients poses a significant challenge. This study proposes a novel approach utilizing deep recurrent autoencoders and a semi-supervised strategy called Gravitational-Wave Anomalous Knowledge (GWAK, reads: guac) to broaden the search for potential anomalies beyond conventional templates.
Key Points
GWAK Methodology
The GWAK method introduces alternative signal priors, capturing essential features of new physics signatures, thereby extending sensitivity beyond pre-computed templates.
Despite a potential decrease in accuracy compared to supervised techniques, GWAK offers qualitative advantages by generalizing experimental sensitivity.
Construction of GWAK Space
The authors construct a low-dimensional embedded space using the GWAK method, which delineates distinct physical signatures of signals along each axis.
This embedded space facilitates the identification of binaries, detector glitches, and exploration of hypothesized astrophysical sources emitting GWs in the interferometer frequency band.
Fig. 1: A 3-dimensional GWAK space with different types of signatures lying in different regions of the GWAK space. Different selection regions can be made to further isolate specific signals, such as Selection Region 2 [green] completely isolating Core-Collapse Supernovae [avocado]
Detection of Anomalies
Five unsupervised autoencoders were trained on datasets comprising background noise, glitches, and three simulated signals representing potential new physics signatures.
The five autoencoders create a 5-dimensional embedding for each incoming event. This allows a final classifier to easily select regions in this space corresponding to anomalous GW events.
The GWAK method efficiently detected anomalies in GW datasets, including unmodeled sources like Core-Collapse Supernovae and White Noise Bursts, while distinguishing signal-like anomalies from detector glitches.
The Future of the GWAK Method Promises Enhanced Efficiency
The GWAK method emerges as a potent tool for anomaly detection in gravitational wave datasets, showcasing its potential to augment existing detection systems. By leveraging deep recurrent autoencoders and alternative signal priors, this approach offers a promising avenue for uncovering hidden astrophysical phenomena and refining our understanding of the universe. Overall, this study marks a significant stride towards enhancing the efficacy and sensitivity of GW detection, opening new horizons for gravitational wave astronomy. The GWAK method doesn’t just improve our existing capabilities; it invites us to imagine what lies beyond the current frontiers of our knowledge.
