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Area of Science:

  • Astrophysics
  • Data Analysis
  • Machine Learning

Background:

  • Deep learning, particularly convolutional networks, is increasingly used for gravitational-wave data analysis.
  • Current methods often treat time-series data as images, which may not be the most efficient approach.

Purpose of the Study:

  • To develop an alternative, computationally efficient framework for gravitational-wave data analysis.
  • To leverage reduced-order modeling and artificial neural networks for parameter estimation.

Main Methods:

  • Representing gravitational waveforms as weighted sums over reduced bases (reduced-order modeling).
  • Training artificial neural networks to map source parameters to basis coefficients.
  • Performing statistical inference directly in the coefficient space.

Main Results:

  • Demonstrated fast and accurate coefficient interpolation for a four-dimensional binary-inspiral waveform family.
  • Neural networks provide analytic waveform derivatives, beneficial for sampling schemes.

Conclusions:

  • The proposed framework offers a theoretically straightforward and computationally efficient alternative for gravitational-wave data analysis.
  • This approach shows promising applications in parameter estimation for gravitational-wave events.