A Neural Network-Based Search for Unmodeled Transients in LIGO-Virgo-KAGRA's Third Observing Run

TL;DR

The paper tackles the challenge of detecting unmodeled short-duration gravitational-wave transients in LVK O3 data by introducing GWAK, a semi-supervised neural-network framework that embeds signals, glitches, and background into a low-dimensional space using multiple autoencoders. It trains on real O3 data with targeted injections and employs a frequency-domain correlation plus a heuristic reweighting scheme to suppress false alarms from glitches, enabling sensitivity to a broad class of bursts beyond CBC templates. The analysis recovers known CBC events, finds no statistically significant non-CBC detections, and reveals GWAK's robustness in high-glitch CAT2 periods, illustrating potential for discovering novel transients in future runs. This work demonstrates that GWAK can complement traditional pipelines, improve anomaly detection in gravitational-wave data, and pave the way for future, more general burst searches.

Abstract

This paper presents the results of a Neural Network (NN)-based search for short-duration gravitational-wave transients in data from the third observing run of LIGO, Virgo, and KAGRA. The search targets unmodeled transients with durations of milliseconds to a few seconds in the 30-1500 Hz frequency band, without assumptions about the incoming signal direction, polarization, or morphology. Using the Gravitational Wave Anomalous Knowledge (GWAK) method, three compact binary coalescences (CBCs) identified by existing pipelines are successfully detected, along with a range of detector glitches. The algorithm constructs a low-dimensional embedded space to capture the physical features of signals, enabling the detection of CBCs, detector glitches, and unmodeled transients. This study demonstrates GWAK's ability to enhance gravitational-wave searches beyond the limits of existing pipelines, laying the groundwork for future detection strategies.

Figures (6)

• Figure 1: The effect of applying the heuristic model on the signal efficiency at a false alarm rate of 1/year as a function of signal-to-noise ratio (SNR). Solid lines represent efficiencies after applying the heuristic model, while dashed lines show efficiencies before applying the heuristic model. Each signal category is represented by a specific color: Binary Black Holes, BBH (yellow), low-frequency sine-Gaussian, SG 64--512 Hz (gray), high-frequency sine-Gaussian, SG 512--1024 Hz (blue), high-frequency white-noise bursts, WNB 400--1000 Hz (pink), Supernova (green), and low-frequency white-noise bursts, WNB 40--400 Hz (red). Shaded regions correspond to statistical uncertainties. The heuristic model improves the separation of background noise from potential signals, leading to better detection performance.
• Figure 2: Cumulative number of events versus False Alarm rate (FAR) found by GWAK analysis using all O3 data. Circular points show results for all data and triangular points show after times around all known CBC sources have been excised. The solid line shows the expected mean value of the background, given the analyzed time. The shaded regions show the 1, 2, and 3 $\sigma$ Poisson uncertainty regions. The plot affirms that all GWAK detections during O3 are consistent with statistical fluctuations and CBC candidates.
• Figure 3: The distribution of events detected by the GWAK algorithm during the O3 of LIGO-Virgo-KAGRA, spanning from April 9, 2019, to March 21, 2020. The vertical axis represents the false alarm rate (FAR), indicating the significance of each detection, with higher FAR values (e.g., $>$ 1/week) corresponding to more significant events. The detections are categorized as baseline O3 detections (purple) and confirmed CBC events (orange).
• Figure 4: Cumulative number of events versus False Alarm rate (FAR) found by GWAK analysis using all CAT2 O3 data. Circular points show results for all CAT2 data. The solid line shows the expected mean value of the background, given the analyzed time. The shaded regions show the 1, 2, and 3 $\sigma$ Poisson uncertainty regions. The plot affirms that all GWAK detections during CAT2 periods are consistent with statistical fluctuations.
• Figure 5: Example of a real BBH merger event (left) and the loudest non-BBH detection (right) identified by the GWAK algorithm in the O3 dataset. The top two panels display the spectrograms of the strain data from H1 and L1, highlighting the characteristic chirp signal of the event across the frequency range over time. The third panel shows the corresponding strain time series for both detectors, illustrating the amplitude evolution of the gravitational wave signal. The bottom panel provides the contributions of different GWAK features (e.g., background, glitch, BBH, sine-Gaussian signals, and frequency-domain correlation) to the final metric. The pronounced dip in the final metric (black curve) corresponds to the detection of the event. As expected, the largest contribution to the final metric for the BBH event (left) originates from the BBH autoencoders, reflecting their design to reconstruct signals with morphology matching binary black hole mergers.