A Recipe to Find Anomalies: Enhancing Gravitational Wave Detection with Deep Recurrent Autoencoders

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.

Affiliations

  1. MIT LIGO Laboratory, USA
  2. Massachusetts Institute of Technology, USA
  3. University of Minnesota, USA
  4. University of Pennsylvania, USA
  5. Carnegie Mellon University