The gravitational wave (GW) detections reported by the LIGO–Virgo–KAGRA (LVK) Collaboration have so far been consistent with quasicircular compact binary coalescences. However, a small fraction of binaries driven to merge through dynamical interactions in dense stellar environments or in field triples may retain measurable orbital eccentricity upon entering the sensitive frequency band of the LVK detectors. A confident measurement of eccentricity in the LVK band would constitute decisive evidence for such dynamically driven mergers. Eccentric waveform models, however, are computationally expensive, so performing production-level inference on all detected signals is an inefficient use of resources when eccentric signals are expected to be rare. In this work, we propose a parsimonious strategy that analyzes the wavelet scattering transform (WST) coefficients of GW signals as an intermediate step between detection and analysis: each signal is assessed for the potential presence of eccentricity in order to provide rapid recommendations for which signals should undergo production-level eccentric inference. By compressing the signals and working with lower-dimensional classifiers, this approach reduces computational complexity and thereby speeds up classification. We evaluate the discriminatory power of the WST using a simple one-dimensional convolutional neural network trained on a large set of synthetic waveforms injected into realistic noise. We find that the WST representation enables effective discrimination between eccentric and quasicircular binaries; we discuss the advantages of its compact, multiscale representation and benchmark it against both simpler and more complex classifiers. Using an effective one-body waveform model, our pipeline achieves a detection accuracy of $\sim 64\%$ at a false-alarm rate of 10%, with an AUC of 0.844 and an average precision of 0.876. Finally, we investigate the WST coefficients’ ability to distinguish eccentricity from spin-induced precession, and find that it performs well across a range of spin-precession magnitudes. We conclude that the WST is a powerful tool for identifying the fingerprints of eccentricity in compact binary coalescence GW signals.

Close

PrevNext

Reuse & PermissionsView figure in article

• 
• 
• 
• 
• 
• 
• 
• 
• 

• Gravitational wave detection
• Gravitational waves
• Gravitational wave detectors
• Machine learning categories