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Related Experiment Videos

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

Manuel D Morales1, Javier M Antelis2, Claudia Moreno1

  • 1Departamento de Física, Centro Universitario de Ciencias Exactas e Ingenierías, Universidad de Guadalajara, Av. Revolución 1500, Guadalajara 44430, Mexico.

Sensors (Basel, Switzerland)
|June 2, 2021
PubMed
Summary
This summary is machine-generated.

Convolutional Neural Networks (CNNs) show high precision in detecting noise but struggle to recall gravitational wave (GW) signals in LIGO data. A resampling white-box approach improves understanding of CNN uncertainties in GW analysis.

Keywords:
Deep LearningLIGO detectorsbinary black holesconvolutional neural networksgravitational wavesprobabilistic binary classificationresampling regimeuncertaintywhite-box testings

Related Experiment Videos

Area of Science:

  • Astrophysics
  • Data Science

Background:

  • Gravitational wave (GW) detection is crucial for understanding cosmic events.
  • Convolutional Neural Networks (CNNs) offer potential for analyzing complex GW data.
  • Understanding uncertainties in CNN models for GW analysis is essential.

Purpose of the Study:

  • To apply CNNs for detecting compact binary coalescence GW signals using real LIGO data.
  • To statistically understand uncertainties in CNNs for GW data analysis via a resampling white-box approach.
  • To evaluate CNN performance under realistic noise conditions.

Main Methods:

  • Utilized single-interferometer data from real LIGO detectors.
  • Converted strain time series to time-frequency images using Morlet wavelets.
  • Employed a resampling white-box approach with repeated k-fold cross-validation.
  • Analyzed data with non-Gaussian noise and hardware injections.

Main Results:

  • Resampling smoothed accuracy perturbations by a factor of 3.6.
  • CNNs demonstrated high precision in noise detection (0.952 for H1, 0.932 for L1).
  • CNNs showed lower sensitivity in recalling GW signals (0.858 for H1, 0.768 for L1), dependent on expected SNR.
  • Model predictions were validated using probabilistic scores, ROC analysis, and t-tests.

Conclusions:

  • CNNs are precise for noise identification but require improvement for sensitive gravitational wave signal recall.
  • The resampling white-box method enhances the statistical understanding of CNN uncertainties in GW analysis.
  • Further research is needed to optimize CNNs for robust GW signal detection in realistic noise environments.