0000000000338021
AUTHOR
Elena Cuoco
showing 3 related works from this author
Total-variation methods for gravitational-wave denoising: Performance tests on Advanced LIGO data
2018
We assess total-variation methods to denoise gravitational-wave signals in real noise conditions, by injecting numerical-relativity waveforms from core-collapse supernovae and binary black hole mergers in data from the first observing run of Advanced LIGO. This work is an extension of our previous investigation where only Gaussian noise was used. Since the quality of the results depends on the regularization parameter of the model, we perform an heuristic search for the value that produces the best results. We discuss various approaches for the selection of this parameter, either based on the optimal, mean, or multiple values, and compare the results of the denoising upon these choices. Mor…
Classification methods for noise transients in advanced gravitational-wave detectors II: performance tests on Advanced LIGO data
2017
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 W…
Application of dictionary learning to denoise LIGO’s blip noise transients
2020
Data streams of gravitational-wave detectors are polluted by transient noise features, or ``glitches,'' of instrumental and environmental origin. In this work we investigate the use of total variation methods and learned dictionaries to mitigate the effect of those transients in the data. We focus on a specific type of transient, ``blip" glitches, as this is the most common type of glitch present in the LIGO detectors and their waveforms are easy to identify. We randomly select 100 blip glitches scattered in the data from advanced LIGO's O1 run, as provided by the citizen-science project Gravity Spy. Our results show that dictionary-learning methods are a valid approach to model and subtrac…