Application of dictionary learning to denoise LIGO’s blip noise transients

Abstract

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 dictionarylearning methods are a valid approach to model and subtract most of the glitch contribution in all cases analyzed, particularly at frequencies below ∼1 kHz. The high-frequency component of the glitch is best removed when a combination of dictionaries with different atom length is employed. As a further example we apply our approach to the glitch visible in the LIGO-Livingston data around the time of merger of binary neutron star signal GW170817, finding satisfactory results. This paper is the first step in our ongoing program to automatically classify and subtract all families of gravitational-wave glitches employing variational methods.

Nonlinear Noise Cleaning in Gravitational-Wave Detectors With Convolutional Neural Networks

“…This would be especially beneficial for searches that require low latency, such as the early warning of binary neutron star mergers (Baltus et al, 2021 ; Yu et al, 2021 ). Other successful usage of ML techniques in GW astronomy include the identification of various GW events (Bayley et al, 2020 ; Chan et al, 2020 ; Dreissigacker and Prix, 2020 ; Huerta et al, 2020 ; Krastev, 2020 ; Schäfer et al, 2020 ; Wong et al, 2020 ; Beheshtipour and Papa, 2021 ; Chang et al, 2021 ; Chatterjee et al, 2021 ; López et al, 2021 ; Marianer et al, 2021 ; Mishra et al, 2021 ; Saiz-Pérez et al, 2021 ; Wei and Huerta, 2021 ; Yan et al, 2021 ), source parameter estimations (Gabbard et al, 2019 ; Chatterjee et al, 2020 ; Chua and Vallisneri, 2020 ; Green et al, 2020 ; Talbot and Thrane, 2020 ; Álvares et al, 2021 ; D'Emilio et al, 2021 ; Krastev et al, 2021 ; Williams et al, 2021 ; Xia et al, 2021 ), and detector characterization (Biswas et al, 2020 ; Colgan et al, 2020 ; Cuoco et al, 2020 ; Essick et al, 2020 ; Torres-Forné et al, 2020 ; Mogushi, 2021 ; Sankarapandian and Kulis, 2021 ; Soni et al, 2021 ; Zhan et al, 2021 ). Besides GW astronomy, the usage of CNNs has led to breakthroughs in a variety of topics related to time-series forecasting and classification (e.g., Refs.…”

Classification of the core-collapse supernova explosion mechanism with learned dictionaries

“…Here, we investigate the performance of an alternative approach to SMEE for the classification of the CCSN explosion mechanism through a (supervised) machine-learning algorithm based on learned dictionaries. We have recently applied this approach for reconstruction and classification of simulated GW signals and actual detector glitches, obtaining promising results (Torres-Forné et al 2016a;Llorens-Monteagudo et al 2019;Torres-Forné et al 2020). Our goal in this paper is to develop a method to denoise numericallygenerated CCSN GW signals and to classify them depending on their morphology.…”

Nonlinear noise regression in gravitational-wave detectors with convolutional neural networks

“…This would be especially beneficial for searches that requires low latency, such as the early warning of binary neutron star mergers [27,14]. Other successful useage of ML techniques in GW astronomy including the identification of various GW events [28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43], source parameter estimations [44,45,46,47,48,49,50,51,52,53], and detector characterization [54,55,56,57,58,59,60,61,62].…”