UniMAP: model-free detection of unclassified noise transients in LIGO-Virgo data using the temporal outlier factor

@article{Ding2021UniMAPMD,
  title={UniMAP: model-free detection of unclassified noise transients in LIGO-Virgo data using the temporal outlier factor},
  author={J Ding and Ray Ng and Jess McIver},
  journal={Classical and Quantum Gravity},
  year={2021},
  volume={39}
}
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: a model-free algorithm to identify and characterize transient noise leveraging the temporal outlier… 
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References

SHOWING 1-10 OF 31 REFERENCES

iDQ: Statistical inference of non-gaussian noise with auxiliary degrees of freedom in gravitational-wave detectors

iDQ is presented, a supervised learning framework to autonomously detect noise artifacts in gravitational-wave detectors based only on auxiliary degrees of freedom insensitive to gravitational waves, and a likelihood-ratio test is constructed that simultaneously accounts for the presence of non-Gaussian noise artifacts.

A hierarchical method for vetoing noise transients in gravitational-wave detectors

Non-Gaussian noise transients in interferometric gravitational-wave detectors increase the background in searches for short-duration and un-modeled signals. We describe a method for vetoing noise…

Omicron: A tool to characterize transient noise in gravitational-wave detectors

Gravity Spy: integrating advanced LIGO detector characterization, machine learning, and citizen science

An innovative project that combines crowdsourcing with machine learning to aid in the challenging task of categorizing all of the glitches recorded by the LIGO detectors is described and a combined method with the aim of improving the efficiency and accuracy of each individual classifier is created.

Utilizing aLIGO glitch classifications to validate gravitational-wave candidates

Advanced LIGO data contains numerous noise transients, or ‘glitches’, that have been shown to reduce the sensitivity of matched filter searches for gravitational waves from compact binaries. These…

Wavelet-Based Classification of Transient Signals for Gravitational Wave Detectors

This paper presents a classification method for short transient signals based on a Wavelet decomposition and de-noising and a classification of the extracted features based on XGBoost algorithm which can be configured as a fast classification system.

Image-based deep learning for classification of noise transients in gravitational wave detectors

A classification pipeline that exploits convolutional neural networks to classify glitches starting from their time-frequency evolution represented as images is presented, showing that the proposed algorithm can automatically classify glitches on very fast timescales and with high accuracy, thus providing a promising tool for online detector characterization.

LIGO detector characterization in the second and third observing runs

The methods used to characterize the LigO detectors and curate the publicly available datasets are presented and the essential role of these datasets in LIGO–Virgo Collaboration analyses of gravitational-waves from both transient and persistent sources is described.

Multiresolution techniques for the detection of gravitational-wave bursts

This is a methodology paper that aims to describe the techniques for identifying significant events as well as the necessary pre-processing that is required in order to improve their performance and uses simulated LIGO noise to illustrate the methods.

GWTC-2.1: Deep Extended Catalog of Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run

The second Gravitational-Wave Transient Catalog, GWTC-2, reported on 39 compact binary coalescences observed by the Advanced LIGO and Advanced Virgo detectors between 1 April 2019 15:00 UTC and 1…