Efficient gravitational-wave glitch identification from environmental data through machine learning

Colgan, R.; Corley, K. R.; Lau, Yenson; Bartos, I.; Wright, John; Márka, Z.; Márka, S.

doi:10.1103/physrevd.101.102003

Cited by 56 publications

(62 citation statements)

References 33 publications

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“…In Sec. 3 we compare the fixed-feature, logistic regression-based model of [16] (which we refer to as FF) to an equivalent linear model with features learned from raw data (which we refer to as LF) and find significant improvement. In Sec.…”

Section: Introductionmentioning

confidence: 97%

Architectural Optimization and Feature Learning for High-Dimensional Time Series Datasets

Colgan¹,

Yan²,

Márka³

et al. 2022

Preprint

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As our ability to sense increases, we are experiencing a transition from data-poor problems, in which the central issue is a lack of relevant data, to data-rich problems, in which the central issue is to identify a few relevant features in a sea of observations. Motivated by applications in gravitational-wave astrophysics, we study a problem in which the goal is to predict the presence of transient noise artifacts in a gravitational wave detector from a rich collection of measurements from the detector and its environment.We argue that feature learning-in which relevant features are optimized from data-is critical to achieving high accuracy. We introduce models that reduce the error rate by over 60% compared to the previous state of the art, which used fixed, hand-crafted features. Feature learning is useful not only because it can improve performance on prediction tasks; the results provide valuable information about patterns associated with phenomena of interest that would otherwise be impossible to discover. In our motivating application, features found to be associated with transient noise provide diagnostic information about its origin and suggest mitigation strategies.Learning in such a high-dimensional setting is challenging. Through experiments with a variety of architectures, we identify two key factors in high-performing models: sparsity, for selecting relevant variables within the high-dimensional observations; and depth, which confers flexibility for handling complex interactions and robustness with respect to temporal variations. We illustrate their significance through a systematic series of experiments on real gravitational-wave detector data. Our results provide experimental corroboration of common assumptions in the machine-learning community and have direct applicability to improving our ability to sense gravitational waves, as well as to a wide variety of problem settings with similarly high-dimensional, noisy, or partly irrelevant data.

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Section: Introductionmentioning

confidence: 97%

Architectural Optimization and Feature Learning for High-Dimensional Time Series Datasets

Colgan¹,

Yan²,

Márka³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

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“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Adhikari

2022

Front. Artif. Intell.

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Currently, the sub-60 Hz sensitivity of gravitational-wave (GW) detectors like Advanced LIGO (aLIGO) is limited by the control noises from auxiliary degrees of freedom which nonlinearly couple to the main GW readout. One promising way to tackle this challenge is to perform nonlinear noise mitigation using convolutional neural networks (CNNs), which we examine in detail in this study. In many cases, the noise coupling is bilinear and can be viewed as a few fast channels' outputs modulated by some slow channels. We show that we can utilize this knowledge of the physical system and adopt an explicit “slow×fast” structure in the design of the CNN to enhance its performance of noise subtraction. We then examine the requirements in the signal-to-noise ratio (SNR) in both the target channel (i.e., the main GW readout) and in the auxiliary sensors in order to reduce the noise by at least a factor of a few. In the case of limited SNR in the target channel, we further demonstrate that the CNN can still reach a good performance if we use curriculum learning techniques, which in reality can be achieved by combining data from quiet times and those from periods with active noise injections.

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“…In particular, the use of Deep Neural Networks (DNN) for classification and/or prediction tasks has become the standard on data analysis applications, ranging from medical diagnosis [10] to particle physics [11]. This trend has now organically been extended to GW astronomy, both for signal detection [12] and for detector characterization, by reducing the impact of noise artifacts or "glitches" of instrumental and environmental origin [13], [14]. Recent approaches to eliminate, or at least mitigate, the effect of glitches are discussed in [15], [16].…”

Section: Introductionmentioning

confidence: 99%

Gravitational-wave parameter inference using Deep Learning

Alvares

Font

Freitas

et al. 2021

2021 International Conference on Content-Based Multimedia Indexing (CBMI)

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We explore machine learning methods to detect gravitational waves (GW) from binary black hole (BBH) mergers using deep learning (DL) algorithms. The DL networks are trained with gravitational waveforms obtained from BBH mergers with component masses randomly sampled in the range from 5 to 100 solar masses and luminosity distances from 100 Mpc to, at least, 2000 Mpc. The GW signal waveforms are injected in public data from the O2 run of the Advanced LIGO and Advanced Virgo detectors, in time windows that do not coincide with those of known detected signals, and the data from each detector in the Advanced LIGO and Advanced Virgo network is combined into a unique RGB image. We show that a classifier network can be trained in order to detect the presence of GW signal with high accuracy. Furthermore, we train a regression network to perform parameter inference on BBH spectrogram data. Without significant optimization of our algorithms we manage to corroborate most of the BBH detections in the GWTC-1 and GWTC-2 catalogs, and obtain parameter inference results that are mostly consistent with published results by the LIGO-Virgo Collaboration in GWTC-1. In particular, our predictions for the chirp mass are compatible (up to 3σ) with the official values for 90% of events.

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Efficient gravitational-wave glitch identification from environmental data through machine learning

Cited by 56 publications

References 33 publications

Architectural Optimization and Feature Learning for High-Dimensional Time Series Datasets

Architectural Optimization and Feature Learning for High-Dimensional Time Series Datasets

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

Gravitational-wave parameter inference using Deep Learning

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