Image-based deep learning for classification of noise transients in gravitational wave detectors

Razzano, M.; Cuoco, E.

doi:10.1088/1361-6382/aab793

Cited by 99 publications

(70 citation statements)

References 64 publications

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“…We used values for τ and K such that the glitches are contained in time windows comparable to those of the CCSN signals. This is compatible with the values in [45] based on comparison with real detector glitches. We define the quality factor of sine Gaussians as Q = 2πf 0 τ and its values are determined by the central glitch frequency f 0 and the exponential decay time constant τ .…”

Section: Data Setssupporting

confidence: 90%

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Core-Collapse supernova gravitational-wave search and deep learning classification

Iess

Cuoco

Morawski

et al. 2020

Mach. Learn.: Sci. Technol.

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We describe a search and classification procedure for gravitational waves emitted by core-collapse supernova (CCSN) explosions, using a convolutional neural network (CNN) combined with an event trigger generator known as Wavelet Detection Filter (WDF). We employ both a 1-D CNN search using time series gravitational-wave data as input, and a 2-D CNN search with time-frequency representation of the data as input. To test the accuracies of our 1-D and 2-D CNN classification, we add CCSN waveforms from the most recent hydrodynamical simulations of neutrino-driven corecollapse to simulated Gaussian colored noise with the Virgo interferometer and the planned Einstein Telescope sensitivity curve. We find classification accuracies, for a single detector, of over ∼ 95% for both 1-D and 2-D CNN pipelines. For the first time in machine learning CCSN studies, we add short duration detector noise transients to our data to test the robustness of our method against false alarms created by detector noise artifacts. Further to this, we show that the CNN can distinguish between different types of CCSN waveform models.

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Section: Data Setssupporting

confidence: 90%

“…These include sine Gaussians and waveforms that are a good representation of scattered light glitches, which are a common problem in GW detectors. The simulated glitches are produced in the same method as [45], and are given by,…”

Section: Data Setsmentioning

confidence: 99%

Core-Collapse supernova gravitational-wave search and deep learning classification

Iess

Cuoco

Morawski

et al. 2020

Mach. Learn.: Sci. Technol.

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“…Finally, there is also a growing body of work which uses CNNs for various tasks that are di erent from but related to a gravitational-wave search, such as glitch classi cation (e.g., [45][46][47][48][49]) or parameter estimation (e.g., [50]). Furthermore, Dreissigacker et al [51] recently presented a proofof-principle study on using convolutional neural networks to search for continuous gravitational waves.…”

Section: Existing Cnn-based Approachesmentioning

confidence: 99%

Convolutional neural networks: A magic bullet for gravitational-wave detection?

et al. 2019

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In the last few years, machine learning techniques, in particular convolutional neural networks, have been investigated as a method to replace or complement traditional matched ltering techniques that are used to detect the gravitational-wave signature of merging black holes. However, to date, these methods have not yet been successfully applied to the analysis of long stretches of data recorded by the Advanced LIGO and Virgo gravitational-wave observatories. In this work, we critically examine the use of convolutional neural networks as a tool to search for merging black holes. We identify the strengths and limitations of this approach, highlight some common pitfalls in translating between machine learning and gravitational-wave astronomy, and discuss the interdisciplinary challenges. In particular, we explain in detail why convolutional neural networks alone cannot be used to claim a statistically signi cant gravitational-wave detection. However, we demonstrate how they can still be used to rapidly ag the times of potential signals in the data for a more detailed follow-up. Our convolutional neural network architecture as well as the proposed performance metrics are better suited for this task than a standard binary classi cations scheme. A detailed evaluation of our approach on Advanced LIGO data demonstrates the potential of such systems as trigger generators. Finally, we sound a note of caution by constructing adversarial examples, which showcase interesting "failure modes" of our model, where inputs with no visible resemblance to real gravitational-wave signals are identi ed as such by the network with high con dence.

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“…Thereafter, CNN algorithms has been applied for several instrumental and physical problems, showing more improvements. For instance, the detection of glitches [ 33 ], trigger generation for locating coalescente time of GWs emitted by BBHs [ 34 ], detection of GWs from BBHs [ 35 ] and Binary neutron star (BNS) systems [ 36 ], detection of GWs emitted by CCSNe and using both phenomenological [ 37 ] and numerical [ 38 ] waveforms, and the detection of continuous GWs from isolated neutron stars [ 39 ], among others.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Gravitational-Wave Data Analysis: A Resampling White-Box Approach

Morales

Antelis

Moreno

et al. 2021

Sensors

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In this work, we apply Convolutional Neural Networks (CNNs) to detect gravitational wave (GW) signals of compact binary coalescences, using single-interferometer data from real LIGO detectors. Here, we adopted a resampling white-box approach to advance towards a statistical understanding of uncertainties intrinsic to CNNs in GW data analysis. We used Morlet wavelets to convert strain time series to time-frequency images. Moreover, we only worked with data of non-Gaussian noise and hardware injections, removing freedom to set signal-to-noise ratio (SNR) values in GW templates by hand, in order to reproduce more realistic experimental conditions. After hyperparameter adjustments, we found that resampling through repeated k-fold cross-validation smooths the stochasticity of mini-batch stochastic gradient descent present in accuracy perturbations by a factor of 3.6. CNNs are quite precise to detect noise, 0.952 for H1 data and 0.932 for L1 data; but, not sensitive enough to recall GW signals, 0.858 for H1 data and 0.768 for L1 data—although recall values are dependent on expected SNR. Our predictions are transparently understood by exploring tthe distribution of probabilistic scores outputted by the softmax layer, and they are strengthened by a receiving operating characteristic analysis and a paired-sample t-test to compare with a random classifier.

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Image-based deep learning for classification of noise transients in gravitational wave detectors

Cited by 99 publications

References 64 publications

Core-Collapse supernova gravitational-wave search and deep learning classification

Core-Collapse supernova gravitational-wave search and deep learning classification

Convolutional neural networks: A magic bullet for gravitational-wave detection?

Deep Learning for Gravitational-Wave Data Analysis: A Resampling White-Box Approach

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