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Noise of non-astrophysical origin contaminates science data taken by the Advanced Laser Interferometer Gravitational-wave Observatory and Advanced Virgo gravitational-wave detectors. Characterization of instrumental and environmental noise transients has proven critical in identifying false positives in the first aLIGO observing run O1. In this talk, we present three algorithms designed for the automatic classification of non-astrophysical transients in advanced detectors. Principal Component Analysis for Transients (PCAT) and an adaptation of LALInference Burst (PC-LIB) are based on Principal Component Analysis. The third algorithm is a combination of a glitch finder called Wavelet Detection Filter (WDF) and unsupervised machine learning techniques for classification.
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This content will become publicly available on December 5, 2025
Performance of iDQ ahead of LIGO, Virgo, and KAGRA's fourth observing run
The gravitational wave detectors used by the LIGO Scientific Collaboration, and the Virgo Collaboration are incredibly sensitive instruments which frequently detect non-stationary, non-Gaussian noise transients. iDQ is a statistical inference framework which leverages the use of auxiliary degrees of freedom monitored in the detectors to identify such transients. In this work, we describe the improvements to the iDQ pipeline made between the third and fourth observing run of the LIGO-Virgo-KAGRA (LVK) collaboration, and show the performance of these changes. We find that iDQ detects a total of 39,398 of the known 100,512 glitches identified by Omicron over the course of the second half of the third observing run. We construct a measure of the probability a glitch is present in the strain data of a given detector by combining information from iDQ and Omicron as well as extend the output of iDQ in a novel method which finds correlations between known glitch classifications, and auxiliary channels. We identify several channels over the course of O3b which frequently record instances of Scattered Light, Whistle, and Blip glitches and discuss use cases for this method in active observing runs.
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- Award ID(s):
- 2409448
- PAR ID:
- 10617027
- Publisher / Repository:
- arXiv e-print
- Date Published:
- Edition / Version:
- 1
- Format(s):
- Medium: X Size: 4MB Other: pdf
- Size(s):
- 4MB
- Institution:
- LIGO Laboratory
- Sponsoring Org:
- National Science Foundation
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