UniMAP: Model-free detection of unclassified noise transients in LIGO-Virgo data using the Temporal Outlier Factor
arXiv:2111.09465 · doi:10.1088/1361-6382/ac7278
Abstract
Data from current gravitational wave detectors contains a high rate of transient noise (glitches) that can trigger false detections and obscure true astrophysical events. Existing noise-detection algorithms largely rely on model-based methods that may miss noise transients unwitnessed by auxiliary sensors or with exotic morphologies. We propose the Unicorn Multi-window Anomaly-detection Pipeline (UniMAP): a model-free algorithm to identify and characterize transient noise leveraging the Temporal Outlier Factor (TOF) via a multi-window data-resampling scheme. We show this windowing scheme extends the anomaly detection capabilities of the TOF algorithm to resolve noise transients of arbitrary morphology and duration. We demonstrate the efficacy of this pipeline in detecting glitches during LIGO and Virgo's third observing run, and discuss potential applications.
16 pages, 6 figures, submitted to Classical and Quantum Gravity
References in corpus (9)
- Advanced Virgo: a 2nd generation interferometric gravitational wave detector
- Advanced LIGO
- GWTC-3: Compact Binary Coalescences Observed by LIGO and Virgo During the Second Part of the Third Observing Run
- Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science
- LIGO Detector Characterization in the Second and Third Observing Runs
- GWTC-2.1: Deep Extended Catalog of Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run
- Low-Latency Gravitational Wave Alerts for Multi-Messenger Astronomy During the Second Advanced LIGO and Virgo Observing Run
- Conformal k-NN Anomaly Detector for Univariate Data Streams
- Incorporation of Statistical Data Quality Information into the GstLAL Search Analysis