D. Trifirò

Classification methods for noise transients in advanced gravitational-wave detectors

Noise of non-astrophysical origin will contaminate science data taken by the Advanced Laser Interferometer Gravitational-wave Observatory (aLIGO) and Advanced Virgo gravitational-wave detectors. Prompt characterization of instrumental and environmental noise transients will be critical for improving the sensitivity of the advanced detectors in the upcoming science runs. During the science runs of the initial gravitational-wave detectors, noise transients were manually classified by visually examining the time-frequency scan of each event. Here, we present three new algorithms designed for the automatic classification of noise transients in advanced detectors. Two of these algorithms are based on Principal Component Analysis. They are Principal Component Analysis for Transients (PCAT), and an adaptation of LALInference Burst (LIB). The third algorithm is a combination of an event generator called Wavelet Detection Filter (WDF) and machine learning techniques for classification. We test these algorithms on simulated data sets, and we show their ability to automatically classify transients by frequency, SNR and waveform morphology.

Classification methods for noise transients in advanced gravitational-wave detectors II: performance tests on Advanced LIGO data

The data taken by the advanced LIGO and Virgo gravitational-wave detectors contains short duration noise transients that limit the significance of astrophysical detections and reduce the duty cycle of the instruments. As the advanced detectors are reaching sensitivity levels that allow for multiple detections of astrophysical gravitational-wave sources it is crucial to achieve a fast and accurate characterization of non-astrophysical transient noise shortly after it occurs in the detectors. Previously we presented three methods for the classification of transient noise sources. They are Principal Component Analysis for Transients (PCAT), Principal Component LALInference Burst (PC-LIB) and Wavelet Detection Filter with Machine Learning (WDF-ML). In this study we carry out the first performance tests of these algorithms on gravitational-wave data from the Advanced LIGO detectors. We use the data taken between the 3rd of June 2015 and the 14th of June 2015 during the 7th engineering run (ER7), and outline the improvements made to increase the performance and lower the latency of the algorithms on real data. This work provides an important test for understanding the performance of these methods on real, non stationary data in preparation for the second advanced gravitational-wave detector observation run, planned for later this year. We show that all methods can classify transients in non stationary data with a high level of accuracy and show the benefits of using multiple classifiers.

Strategy for signal classification to improve data quality for Advanced Detectors gravitational-wave searches

Elena Cuoco()(), Jade Powell(), Alejandro Torres-Forné(), Ryan Lynch(), Daniele Trifirò()(), Marco Cavaglià(), Ik Siong Heng() and José A. Font()() () European Gravitational Observatory (EGO) Via E. Amaldi, I-56021 Cascina, (PI) Italy () Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Pisa Edificio C, Largo B. Pontecorvo 3, 56127 Pisa, Italy () SUPA, Institute for Gravitational Research, School of Physics and Astronomy, University of Glasgow, Glasgow, G12 8QQ, Scotland, UK () Departamento de Astronomı́a y Astrof́ısica, Universitat de València Dr. Moliner 50, 46100, Burjassot (València), Spain () Massachusetts Institute of Technology 185 Albany St, 02139 Cambridge MA, USA () Dipartimento di Fisica E. Fermi, Università di Pisa Pisa 56127, Italy () Department of Physics and Astronomy, The University of Mississippi University, MS 38677, USA () Observatori Astronòmic, Universitat de València C/ Catedrático José Beltrán 2, 46980, Paterna (València), Spain