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This paper presents a methodology that combines topological data analysis and convolutional neural networks (CNNs) to improve gravitational wave detection. Using persistent homology, topological features are extracted and integrated into a CNN model, improving its ability to distinguish relevant signals in the presence of noise. Experiments show that this approach not only increases signal detection accuracy, but also reduces the computational complexity involved in letting the neural network learn without the aid of persistent homology. These findings open up possibilities for efficient gravitational wave detection in noisy signal analysis.

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Topological Data Analysis and Convolutional Neural Networks for Gravitational Wave Detection

  • 会议论文

  • 发表日期：

    2026年

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  • Felipe de Jesús Felix Arredondo,
  • Sofia Alvarez Sandoval,
  • David Alejandro Matamoros Alvarado,
  • Ana Almeida-Perez,
  • Valeria María Serna Salazar,
  • Raúl Alejandro Pérez Saucedo,
  • Alejandro Ucan-Puc,
  • Rodrigo Davila Figueroa

摘要

This paper presents a methodology that combines topological data analysis and convolutional neural networks (CNNs) to improve gravitational wave detection. Using persistent homology, topological features are extracted and integrated into a CNN model, improving its ability to distinguish relevant signals in the presence of noise. Experiments show that this approach not only increases signal detection accuracy, but also reduces the computational complexity involved in letting the neural network learn without the aid of persistent homology. These findings open up possibilities for efficient gravitational wave detection in noisy signal analysis.