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

Manuel D. Morales, Javier Mauricio Antelis, Claudia Moreno, Alexander I. Nesterov

Sensors 2021 article cited by 1

Source Open-access PDF More in Physics & Astronomy

importance 7/10 partially solved Pulsars and Gravitational Waves Research gravitational-wave astronomy stage: research

Problem

Detecting weak gravitational-wave signals from compact binary coalescences in noisy single-interferometer LIGO detector data.

Why it matters

Reliable signal detection is essential for observing astrophysical events in gravitational-wave astronomy.

What the AI did

CNNs classified Morlet-wavelet time-frequency images of real LIGO strain data (non-Gaussian noise, hardware injections) to detect GW signals; repeated k-fold resampling quantified training stochasticity.

Results

Noise-detection precision 0.952 (H1) and 0.932 (L1); resampling smoothed accuracy perturbations by factor 3.6; GW signal recall insufficient (abstract truncated).

Impact

Shows CNNs can precisely identify noise in real LIGO data but cannot yet reliably recall GW signals.

Record

Venue	Sensors
Published	2021
Work type	article
Authors	Manuel D. Morales, Javier Mauricio Antelis, Claudia Moreno, Alexander I. Nesterov
Citations	1 (OpenAlex, at import time)
References	94
Model or method	Convolutional Neural Networks (CNNs) with Morlet wavelet transforms
Organization	not stated in source
Primary source	https://doi.org/10.3390/s21093174
Open access	gold
Keywords	gravitational waves, convolutional neural networks, LIGO, signal detection, uncertainty

Importance rating

7 / 10

significant — thousands to millions affected

GW detection underpins gravitational-wave astronomy, a foundational physics field; abstract demonstrates method on real LIGO data but states no impact scale beyond the detectors themselves.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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