Improvement of multiscale decomposition for space-based gravitational wave signal processing technology

Area of Science:

• Astrophysics and Signal Processing
• Gravitational Wave Astronomy

Background:

• Space-based gravitational wave detection faces significant noise challenges from environmental and instrumental factors.
• Accurate detection of faint gravitational wave signals requires effective noise suppression techniques.

Purpose of the Study:

• To propose and evaluate a novel multiscale variational mode adaptive denoising algorithm for gravitational wave signals.
• To enhance the quality and reliability of gravitational wave data by mitigating noise.

Main Methods:

• Developed a multiscale variational mode adaptive denoising algorithm integrating momentum gradient descent.
• Incorporated momentum factors and multiscale concepts to overcome local optima and improve convergence.
• Combined with the least mean squares algorithm for adaptive weight adjustment to mitigate diverse noise sources.

Main Results:

• The proposed algorithm demonstrated superior noise suppression capabilities compared to existing methods.
• Effectively reduced noise in simulated and real gravitational wave signals.
• Significantly enhanced the signal-to-noise ratio and reliability of gravitational wave data.

Conclusions:

• The multiscale variational mode adaptive denoising algorithm is highly effective for space-based gravitational wave detection.
• This method meets the stringent noise suppression requirements for future gravitational wave observatories.
• Offers a promising solution for improving the accuracy and sensitivity of gravitational wave astronomy.