Cuoco, Elena and Powell, Jade and Torres-Forné, Alejandro and Lynch, Ryan and Trifirò,, Daniele and Cavaglià, Marco and Siong Heng, Ik and Font, José A. (2017) Strategy for signal classification to improve data quality for Advanced Detectors gravitational-wave searches. Il nuovo cimento C, 40 (3). pp. 1-5. ISSN 1826-9885
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Abstract
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.
| Item Type: | Article |
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| Subjects: | 500 Scienze naturali e Matematica > 530 Fisica |
| Depositing User: | Marina Spanti |
| Date Deposited: | 17 Nov 2020 09:55 |
| Last Modified: | 17 Nov 2020 09:55 |
| URI: | http://eprints.bice.rm.cnr.it/id/eprint/19958 |
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